XEM: An explainable-by-design ensemble method for multivariate time series classification
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conqu...
Saved in:
| Published in | Data mining and knowledge discovery Vol. 36; no. 3; pp. 917 - 957 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2022
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability-by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise). |
|---|---|
| AbstractList | We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise). |
| Author | Masson, Véronique Fromont, Élisa Faverdin, Philippe Termier, Alexandre Fauvel, Kevin |
| Author_xml | – sequence: 1 givenname: Kevin surname: Fauvel fullname: Fauvel, Kevin email: kevin.fauvel@inria.fr organization: Inria, Univ Rennes, CNRS, IRISA – sequence: 2 givenname: Élisa surname: Fromont fullname: Fromont, Élisa organization: Univ Rennes, IUF, Inria, CNRS, IRISA – sequence: 3 givenname: Véronique surname: Masson fullname: Masson, Véronique organization: Inria, Univ Rennes, CNRS, IRISA – sequence: 4 givenname: Philippe surname: Faverdin fullname: Faverdin, Philippe organization: PEGASE, INRAE, AGROCAMPUS OUEST – sequence: 5 givenname: Alexandre surname: Termier fullname: Termier, Alexandre organization: Inria, Univ Rennes, CNRS, IRISA |
| BackLink | https://inria.hal.science/hal-03599214$$DView record in HAL |
| BookMark | eNp9kE1rFTEUhoNUsK3-AVcDrlxET3ImmYy7S6mtcMWNhXYVMpkzbcp8XJPcYv99czui4KKrhDfvc3J4TtjRvMzE2HsBnwRA8zkJ0MJwkJIDGIlcv2LHQjXIG6Wvj8odTc2VEfCGnaR0DwBKIhyzm-vz71-qzVzR793owuy6kXj3yHtK4bakc6KpRNVE-W7pq2GJ1bQfc3hwMbhMVQ4TVYlioFT50aUUhuBdDsv8lr0e3Jjo3Z_zlF19Pf95dsm3Py6-nW223KPCzJu2FiSUhqZHKaBzhN40vTdDJ_taeRSI6IDQtZ0H0KZxA5YXo6nufN3hKfu4zr1zo93FMLn4aBcX7OVmaw8ZoGpbKeoHUbof1u4uLr_2lLK9X_ZxLutZqXXdKlWbQ0uuLR-XlCINf8cKsAfddtVti277rNvqApn_IB_ys4gcXRhfRnFFU_lnvqX4b6sXqCcKMpWN |
| CitedBy_id | crossref_primary_10_1002_ohn_840 crossref_primary_10_3390_bdcc7040173 crossref_primary_10_3390_app13095436 crossref_primary_10_1007_s10618_023_00969_x crossref_primary_10_1016_j_pacfin_2024_102612 crossref_primary_10_1016_j_neunet_2024_106896 crossref_primary_10_1109_TCDS_2024_3370219 crossref_primary_10_1109_TIM_2022_3201203 crossref_primary_10_3390_s23125621 crossref_primary_10_1115_1_4066947 crossref_primary_10_1109_TAI_2024_3360180 crossref_primary_10_1016_j_eja_2024_127338 crossref_primary_10_3390_app13126983 crossref_primary_10_3390_su152115386 crossref_primary_10_1007_s10661_023_11388_2 crossref_primary_10_1007_s13143_024_00351_x crossref_primary_10_3233_IDT_240224 crossref_primary_10_3390_app14093797 crossref_primary_10_1007_s10994_023_06502_x crossref_primary_10_1016_j_ijdrr_2023_104109 crossref_primary_10_1016_j_ins_2023_119354 crossref_primary_10_1061_JSDCCC_SCENG_1611 crossref_primary_10_1109_TSMC_2023_3342640 crossref_primary_10_1016_j_ins_2022_09_009 crossref_primary_10_1016_j_knosys_2024_112015 crossref_primary_10_1016_j_jappgeo_2023_105143 crossref_primary_10_1016_j_ecoenv_2023_115250 crossref_primary_10_1016_j_jhep_2023_10_004 |
| Cites_doi | 10.1109/MIS.2019.2957223 10.1007/s10115-019-01389-4 10.1007/3-540-45014-9_1 10.1007/BF00058655 10.1023/A:1007652114878 10.1609/aaai.v34i04.6165 10.1145/2939672.2939785 10.1145/3219819.3219831 10.1145/3132847.3132980 10.1145/3292500.3330654 10.1016/j.artint.2018.07.007 10.1162/neco.1991.3.1.79 10.1007/s10618-016-0473-y 10.1007/s10462-012-9338-y 10.1111/j.1467-9868.2005.00503.x 10.1023/A:1010933404324 10.1016/S0893-6080(99)00073-8 10.1016/j.neunet.2019.04.014 10.1007/s11263-019-01228-7 10.1007/s10618-016-0455-0 10.1007/s10618-015-0425-y 10.1002/widm.1143 10.1609/aaai.v34i01.5376 10.1145/2247596.2247656 10.1109/SSCI.2015.199 10.1109/IJCNN.2017.7966039 10.1609/aaai.v32i1.11491 10.1109/CVPR.2016.90 10.1145/3447548.3467401 10.1038/s41591-018-0316-z 10.1017/S0269888997003123 10.1007/s10618-014-0349-y 10.1016/j.patcog.2017.08.016 10.1038/s42256-019-0048-x 10.1145/3359786 10.1145/2939672.2939778 10.1145/3292500.3330712 10.1162/neco.1996.8.7.1341 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022. Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 – notice: The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022. – notice: Distributed under a Creative Commons Attribution 4.0 International License |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U 1XC VOOES |
| DOI | 10.1007/s10618-022-00823-6 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Research Library ProQuest Central ProQuest Central UK/Ireland Health Research Premium Collection ProQuest Central Essentials - QC ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Research Library (ProQuest) Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics Computer Science |
| EISSN | 1573-756X |
| EndPage | 957 |
| ExternalDocumentID | oai_HAL_hal_03599214v1 10_1007_s10618_022_00823_6 |
| GrantInformation_xml | – fundername: Agence Nationale de la Recherche grantid: ANR-16-CONV-0004 funderid: http://dx.doi.org/10.13039/501100001665 |
| GroupedDBID | -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 203 29F 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5VS 67Z 6NX 78A 7WY 8AO 8FE 8FG 8FL 8G5 8TC 8UJ 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EDO EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X J-C J0Z J9A JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV LAK LLZTM M0C M0N M2O M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM OVD P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOS R89 R9I RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S27 S3B SAP SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7W Z7X Z7Y Z7Z Z81 Z83 Z88 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ACSTC ADHKG ADKFA AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT 7SC 7XB 8AL 8FD 8FK ABRTQ JQ2 L.- L7M L~C L~D MBDVC PKEHL PQEST PQGLB PQUKI Q9U 1XC VOOES |
| ID | FETCH-LOGICAL-c353t-7941e15607d3210bae3c87dc8fb2d45c31333a0e3a9bc00687af32d486e4bc4b3 |
| IEDL.DBID | U2A |
| ISSN | 1384-5810 |
| IngestDate | Fri May 09 12:20:55 EDT 2025 Sat Aug 16 15:52:58 EDT 2025 Tue Jul 01 00:40:32 EDT 2025 Thu Apr 24 22:58:25 EDT 2025 Fri Feb 21 02:45:15 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Multivariate time series Ensemble learning Explainability Classification Multivariate Time Series Ensemble Learning |
| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c353t-7941e15607d3210bae3c87dc8fb2d45c31333a0e3a9bc00687af32d486e4bc4b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-3883-8389 0000-0003-0133-3491 0000-0003-1784-0017 |
| OpenAccessLink | https://inria.hal.science/hal-03599214 |
| PQID | 2664955481 |
| PQPubID | 43030 |
| PageCount | 41 |
| ParticipantIDs | hal_primary_oai_HAL_hal_03599214v1 proquest_journals_2664955481 crossref_primary_10_1007_s10618_022_00823_6 crossref_citationtrail_10_1007_s10618_022_00823_6 springer_journals_10_1007_s10618_022_00823_6 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-01 |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Data mining and knowledge discovery |
| PublicationTitleAbbrev | Data Min Knowl Disc |
| PublicationYear | 2022 |
| Publisher | Springer US Springer Nature B.V Springer |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V – name: Springer |
| References | Shokoohi-YektaMHuBJinHWangJKeoghEGeneralizing DTW to the multi-dimensional case requires an adaptive approachData Min Knowl Disc201731131359681510.1007/s10618-016-0455-0 Zerveas G, Jayaraman S, Patel D, Bhamidipaty A, Eickhoff C (2021) A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining KarimFMajumdarSDarabiHHarfordSMultivariate LSTM-FCNs for time series classificationNeural Netw201911623724510.1016/j.neunet.2019.04.014 Dua D, Graff C (2017) UCI machine learning repository KarlssonIRebaneJPapapetrouPGionisALocally and globally explainable time series tweakingKnowl Inf Syst2020621671170010.1007/s10115-019-01389-4 Jiang R, Song X, Huang D, Song X, Xia T, Cai Z, Wang Z, Kim K, Shibasaki R (2019) DeepUrbanEvent: a system for predicting citywide crowd dynamics at big events. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining KotsiantisSPintelasPCombining bagging and boostingInt J Comput Intell200518372381 Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res Schäfer P, Högqvist M (2012) SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th international conference on extending database technology, pp 516–527 SharkeyASharkeyNCombining diverse neural netsKnowl Eng Rev199712323124710.1017/S0269888997003123 LiuYYaoXEnsemble learning via negative correlationNeural Netw199912101399140410.1016/S0893-6080(99)00073-8 Fauvel K, Masson V, Fromont É, Faverdin P, Termier A (2019) Towards sustainable dairy management - a machine learning enhanced method for estrus detection. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining Schäfer P, Leser U (2017) Multivariate time series classification with WEASEL+MUSE MasoudniaSEbrahimpourRMixture of experts: a literature surveyArtif Intell Rev201442227529310.1007/s10462-012-9338-y SelvarajuRDasAVedantamRCogswellMParikhDBatraDGrad-CAM: visual explanations from deep networks via gradient-based localizationInt J Comput Vision201912833635910.1007/s11263-019-01228-7 Fauvel K, Balouek-Thomert D, Melgar D, Silva P, Simonet A, Antoniu G, Costan A, Masson V, Parashar M, Rodero I, Termier A (2020a) A distributed multi-sensor machine learning approach to earthquake early warning. In: Proceedings of the 34th AAAI conference on artificial intelligence BaydoganMRungerGTime series representation and similarity based on local autopatternsData Min Knowl Disc2016302476509345820210.1007/s10618-015-0425-y SesmeroMLedezmaASanchisAGenerating ensembles of heterogeneous classifiers using stacked generalizationWiley Interdiscip Rev Data Min Knowl Discov201551213410.1002/widm.1143 TuncelKBaydoganMAutoregressive forests for multivariate time series modelingPattern Recogn20187320221510.1016/j.patcog.2017.08.016 ZouHHastieTRegularization and variable selection via the elastic netJ R Stat Soc Ser B (Stat Methodol)2005672301320213732710.1111/j.1467-9868.2005.00503.x Zhang X, Gao Y, Lin J, Lu C (2020) TapNet: multivariate time series classification with attentional prototypical network. In: Proceedings of the 34th AAAI conference on artificial intelligence MillerTExplanation in artificial intelligence: insights from the social sciencesArtif Intell2019267138387451110.1016/j.artint.2018.07.007 He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition RudinCStop explaining black box machine learning models for high stakes decisions and use interpretable models insteadNat Mach Intell2019120621510.1038/s42256-019-0048-x GamaJBrazdilPCascade generalizationMach Learn200041331534310.1023/A:1007652114878 DemšarJStatistical comparisons of classifiers over multiple data setsJ Mach Learn Res2006713022743601222.68184 Bagnall A, Lines J, Keogh E (2018) The UEA UCR time series classification archive Ribeiro M, Singh S, Guestrin C (2016) “Why should i trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining Breiman L (2001) Random forests. Mach Learn, pp 5–32 EbrahimpourRSadeghnejadNAraniSMohammadiNBoost-wise pre-loaded mixture of experts for classification tasksNeural Comput Appl2012221365377 Bergstra J, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of the 25th international conference on neural information processing systems Ransbotham S, Khodabandeh S, Fehling R, LaFountain B, Kiron D (2019) Winning with AI. In: MIT sloan management review and boston consulting group Lundberg S, Lee S (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: Proceedings of the 2017 international joint conference on neural networks Fauvel K, Masson V, Fromont É (2020b) A performance-explainability framework to benchmark machine learning methods: application to multivariate time series classifiers. In: Proceedings of the IJCAI-PRICAI workshop on explainable artificial intelligence Ribeiro M, Singh S, Guestrin C (2018) Anchors: high-precision model-agnostic explanations. In: Proceedings of the 32nd AAAI conference on artificial intelligence JacobsRJordanMNowlanSHintonGAdaptive mixtures of local expertsNeural Comput199131798710.1162/neco.1991.3.1.79 Breiman L (1996) Bagging predictors. Mach Learn, pp 123–140 SchapireRThe strength of weak learnabilityMach Learn19905197227 Dietterich T (2000) Ensemble methods in machine learning. Multiple Classifier Syst, pp 1–15 WolpertDThe lack of a priori distinctions between learning algorithmsNeural Comput1996871341139010.1162/neco.1996.8.7.1341 Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and regression trees. The Wadsworth and Brooks-Cole statistics-probability series. Taylor & Francis Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning EstevaARobicquetARamsundarBKuleshovVDePristoMChouKCuiCCorradoGThrunSDeanJA guide to deep learning in healthcareNat Med201925242910.1038/s41591-018-0316-z BaydoganMRungerGLearning a symbolic representation for multivariate time series classificationData Min Knowl Disc2014292400422331246610.1007/s10618-014-0349-y Du M, Liu N, Hu X (2020) Techniques for interpretable machine learning. Commun ACM KarlssonIPapapetrouPBoströmHGeneralized random shapelet forestsData Min Knowl Disc201630510531085353997310.1007/s10618-016-0473-y Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining Seto S, Zhang W, Zhou Y (2015) Multivariate time series classification using dynamic time warping template selection for human activity recognition. In: Proceedings of the 2015 IEEE symposium series on computational intelligence Lipton Z (2016) The mythos of model interpretability. In: Proceedings of the ICML workshop on human interpretability in machine learning Cussins Newman J (2019) Toward AI security: global aspirations for a more resilient future. In: Center for long-term cybersecurity GuidottiRMonrealeAGiannottiFPedreschiDRuggieriSTuriniFFactual and counterfactual explanations for black box decision makingIEEE Intell Syst2019346142310.1109/MIS.2019.2957223 Wistuba M, Grabocka J, Schmidt-Thieme L (2015) Ultra-fast shapelets for time series classification Li J, Rong Y, Meng H, Lu Z, Kwok T, Cheng H (2018) TATC: Predicting Alzheimer’s disease with actigraphy data. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining Zhang H (2004) The optimality of Naïve Bayes. In: Proceedings of the 17th Florida artificial intelligence research society conference 823_CR38 M Baydogan (823_CR3) 2016; 30 823_CR36 D Wolpert (823_CR51) 1996; 8 823_CR37 823_CR30 S Kotsiantis (823_CR28) 2005; 1 I Karlsson (823_CR26) 2016; 30 823_CR35 823_CR32 M Shokoohi-Yekta (823_CR47) 2017; 31 823_CR49 M Baydogan (823_CR2) 2014; 29 K Tuncel (823_CR48) 2018; 73 J Gama (823_CR20) 2000; 41 823_CR41 823_CR40 R Ebrahimpour (823_CR14) 2012; 22 823_CR45 J Demšar (823_CR10) 2006; 7 F Karim (823_CR25) 2019; 116 R Jacobs (823_CR23) 1991; 3 H Zou (823_CR55) 2005; 67 A Esteva (823_CR15) 2019; 25 R Guidotti (823_CR21) 2019; 34 823_CR16 A Sharkey (823_CR46) 1997; 12 823_CR17 823_CR18 823_CR19 823_CR7 823_CR52 823_CR8 823_CR53 823_CR5 823_CR50 823_CR6 M Sesmero (823_CR44) 2015; 5 823_CR12 823_CR13 823_CR9 823_CR54 823_CR11 R Selvaraju (823_CR43) 2019; 128 Y Liu (823_CR31) 1999; 12 C Rudin (823_CR39) 2019; 1 823_CR29 T Miller (823_CR34) 2019; 267 R Schapire (823_CR42) 1990; 5 823_CR24 823_CR22 I Karlsson (823_CR27) 2020; 62 823_CR4 823_CR1 S Masoudnia (823_CR33) 2014; 42 |
| References_xml | – reference: BaydoganMRungerGTime series representation and similarity based on local autopatternsData Min Knowl Disc2016302476509345820210.1007/s10618-015-0425-y – reference: Schäfer P, Högqvist M (2012) SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th international conference on extending database technology, pp 516–527 – reference: Ransbotham S, Khodabandeh S, Fehling R, LaFountain B, Kiron D (2019) Winning with AI. In: MIT sloan management review and boston consulting group – reference: SharkeyASharkeyNCombining diverse neural netsKnowl Eng Rev199712323124710.1017/S0269888997003123 – reference: Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning – reference: Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining – reference: DemšarJStatistical comparisons of classifiers over multiple data setsJ Mach Learn Res2006713022743601222.68184 – reference: GamaJBrazdilPCascade generalizationMach Learn200041331534310.1023/A:1007652114878 – reference: Ribeiro M, Singh S, Guestrin C (2018) Anchors: high-precision model-agnostic explanations. In: Proceedings of the 32nd AAAI conference on artificial intelligence – reference: JacobsRJordanMNowlanSHintonGAdaptive mixtures of local expertsNeural Comput199131798710.1162/neco.1991.3.1.79 – reference: KarlssonIRebaneJPapapetrouPGionisALocally and globally explainable time series tweakingKnowl Inf Syst2020621671170010.1007/s10115-019-01389-4 – reference: TuncelKBaydoganMAutoregressive forests for multivariate time series modelingPattern Recogn20187320221510.1016/j.patcog.2017.08.016 – reference: EbrahimpourRSadeghnejadNAraniSMohammadiNBoost-wise pre-loaded mixture of experts for classification tasksNeural Comput Appl2012221365377 – reference: He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition – reference: Breiman L (2001) Random forests. Mach Learn, pp 5–32 – reference: Shokoohi-YektaMHuBJinHWangJKeoghEGeneralizing DTW to the multi-dimensional case requires an adaptive approachData Min Knowl Disc201731131359681510.1007/s10618-016-0455-0 – reference: Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: Proceedings of the 2017 international joint conference on neural networks – reference: Schäfer P, Leser U (2017) Multivariate time series classification with WEASEL+MUSE – reference: MillerTExplanation in artificial intelligence: insights from the social sciencesArtif Intell2019267138387451110.1016/j.artint.2018.07.007 – reference: Zhang H (2004) The optimality of Naïve Bayes. In: Proceedings of the 17th Florida artificial intelligence research society conference – reference: Fauvel K, Masson V, Fromont É (2020b) A performance-explainability framework to benchmark machine learning methods: application to multivariate time series classifiers. In: Proceedings of the IJCAI-PRICAI workshop on explainable artificial intelligence – reference: Wistuba M, Grabocka J, Schmidt-Thieme L (2015) Ultra-fast shapelets for time series classification – reference: BaydoganMRungerGLearning a symbolic representation for multivariate time series classificationData Min Knowl Disc2014292400422331246610.1007/s10618-014-0349-y – reference: SelvarajuRDasAVedantamRCogswellMParikhDBatraDGrad-CAM: visual explanations from deep networks via gradient-based localizationInt J Comput Vision201912833635910.1007/s11263-019-01228-7 – reference: EstevaARobicquetARamsundarBKuleshovVDePristoMChouKCuiCCorradoGThrunSDeanJA guide to deep learning in healthcareNat Med201925242910.1038/s41591-018-0316-z – reference: Fauvel K, Balouek-Thomert D, Melgar D, Silva P, Simonet A, Antoniu G, Costan A, Masson V, Parashar M, Rodero I, Termier A (2020a) A distributed multi-sensor machine learning approach to earthquake early warning. In: Proceedings of the 34th AAAI conference on artificial intelligence – reference: Li J, Rong Y, Meng H, Lu Z, Kwok T, Cheng H (2018) TATC: Predicting Alzheimer’s disease with actigraphy data. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining – reference: SesmeroMLedezmaASanchisAGenerating ensembles of heterogeneous classifiers using stacked generalizationWiley Interdiscip Rev Data Min Knowl Discov201551213410.1002/widm.1143 – reference: Seto S, Zhang W, Zhou Y (2015) Multivariate time series classification using dynamic time warping template selection for human activity recognition. In: Proceedings of the 2015 IEEE symposium series on computational intelligence – reference: Bagnall A, Lines J, Keogh E (2018) The UEA UCR time series classification archive – reference: WolpertDThe lack of a priori distinctions between learning algorithmsNeural Comput1996871341139010.1162/neco.1996.8.7.1341 – reference: SchapireRThe strength of weak learnabilityMach Learn19905197227 – reference: Fauvel K, Masson V, Fromont É, Faverdin P, Termier A (2019) Towards sustainable dairy management - a machine learning enhanced method for estrus detection. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining – reference: KarimFMajumdarSDarabiHHarfordSMultivariate LSTM-FCNs for time series classificationNeural Netw201911623724510.1016/j.neunet.2019.04.014 – reference: KotsiantisSPintelasPCombining bagging and boostingInt J Comput Intell200518372381 – reference: Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and regression trees. The Wadsworth and Brooks-Cole statistics-probability series. Taylor & Francis – reference: Ribeiro M, Singh S, Guestrin C (2016) “Why should i trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining – reference: ZouHHastieTRegularization and variable selection via the elastic netJ R Stat Soc Ser B (Stat Methodol)2005672301320213732710.1111/j.1467-9868.2005.00503.x – reference: KarlssonIPapapetrouPBoströmHGeneralized random shapelet forestsData Min Knowl Disc201630510531085353997310.1007/s10618-016-0473-y – reference: Lundberg S, Lee S (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems – reference: Du M, Liu N, Hu X (2020) Techniques for interpretable machine learning. Commun ACM – reference: Zhang X, Gao Y, Lin J, Lu C (2020) TapNet: multivariate time series classification with attentional prototypical network. In: Proceedings of the 34th AAAI conference on artificial intelligence – reference: LiuYYaoXEnsemble learning via negative correlationNeural Netw199912101399140410.1016/S0893-6080(99)00073-8 – reference: Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res – reference: Zerveas G, Jayaraman S, Patel D, Bhamidipaty A, Eickhoff C (2021) A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining – reference: Breiman L (1996) Bagging predictors. Mach Learn, pp 123–140 – reference: RudinCStop explaining black box machine learning models for high stakes decisions and use interpretable models insteadNat Mach Intell2019120621510.1038/s42256-019-0048-x – reference: GuidottiRMonrealeAGiannottiFPedreschiDRuggieriSTuriniFFactual and counterfactual explanations for black box decision makingIEEE Intell Syst2019346142310.1109/MIS.2019.2957223 – reference: MasoudniaSEbrahimpourRMixture of experts: a literature surveyArtif Intell Rev201442227529310.1007/s10462-012-9338-y – reference: Lipton Z (2016) The mythos of model interpretability. In: Proceedings of the ICML workshop on human interpretability in machine learning – reference: Dua D, Graff C (2017) UCI machine learning repository – reference: Bergstra J, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of the 25th international conference on neural information processing systems – reference: Jiang R, Song X, Huang D, Song X, Xia T, Cai Z, Wang Z, Kim K, Shibasaki R (2019) DeepUrbanEvent: a system for predicting citywide crowd dynamics at big events. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining – reference: Cussins Newman J (2019) Toward AI security: global aspirations for a more resilient future. In: Center for long-term cybersecurity – reference: Dietterich T (2000) Ensemble methods in machine learning. Multiple Classifier Syst, pp 1–15 – ident: 823_CR9 – volume: 22 start-page: 365 issue: 1 year: 2012 ident: 823_CR14 publication-title: Neural Comput Appl – ident: 823_CR18 – volume: 34 start-page: 14 issue: 6 year: 2019 ident: 823_CR21 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2019.2957223 – volume: 62 start-page: 1671 year: 2020 ident: 823_CR27 publication-title: Knowl Inf Syst doi: 10.1007/s10115-019-01389-4 – ident: 823_CR11 doi: 10.1007/3-540-45014-9_1 – volume: 1 start-page: 372 issue: 8 year: 2005 ident: 823_CR28 publication-title: Int J Comput Intell – ident: 823_CR5 doi: 10.1007/BF00058655 – ident: 823_CR1 – volume: 41 start-page: 315 issue: 3 year: 2000 ident: 823_CR20 publication-title: Mach Learn doi: 10.1023/A:1007652114878 – ident: 823_CR54 doi: 10.1609/aaai.v34i04.6165 – ident: 823_CR8 doi: 10.1145/2939672.2939785 – ident: 823_CR29 doi: 10.1145/3219819.3219831 – ident: 823_CR41 doi: 10.1145/3132847.3132980 – ident: 823_CR24 doi: 10.1145/3292500.3330654 – volume: 267 start-page: 1 year: 2019 ident: 823_CR34 publication-title: Artif Intell doi: 10.1016/j.artint.2018.07.007 – ident: 823_CR13 – volume: 3 start-page: 79 issue: 1 year: 1991 ident: 823_CR23 publication-title: Neural Comput doi: 10.1162/neco.1991.3.1.79 – ident: 823_CR4 – volume: 30 start-page: 1053 issue: 5 year: 2016 ident: 823_CR26 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-016-0473-y – volume: 42 start-page: 275 issue: 2 year: 2014 ident: 823_CR33 publication-title: Artif Intell Rev doi: 10.1007/s10462-012-9338-y – volume: 67 start-page: 301 issue: 2 year: 2005 ident: 823_CR55 publication-title: J R Stat Soc Ser B (Stat Methodol) doi: 10.1111/j.1467-9868.2005.00503.x – ident: 823_CR6 doi: 10.1023/A:1010933404324 – volume: 12 start-page: 1399 issue: 10 year: 1999 ident: 823_CR31 publication-title: Neural Netw doi: 10.1016/S0893-6080(99)00073-8 – ident: 823_CR30 – volume: 116 start-page: 237 year: 2019 ident: 823_CR25 publication-title: Neural Netw doi: 10.1016/j.neunet.2019.04.014 – volume: 128 start-page: 336 year: 2019 ident: 823_CR43 publication-title: Int J Comput Vision doi: 10.1007/s11263-019-01228-7 – ident: 823_CR7 – volume: 31 start-page: 1 year: 2017 ident: 823_CR47 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-016-0455-0 – ident: 823_CR35 – volume: 30 start-page: 476 issue: 2 year: 2016 ident: 823_CR3 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-015-0425-y – volume: 5 start-page: 21 issue: 1 year: 2015 ident: 823_CR44 publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1143 – ident: 823_CR17 doi: 10.1609/aaai.v34i01.5376 – ident: 823_CR40 doi: 10.1145/2247596.2247656 – ident: 823_CR45 doi: 10.1109/SSCI.2015.199 – ident: 823_CR49 doi: 10.1109/IJCNN.2017.7966039 – ident: 823_CR38 doi: 10.1609/aaai.v32i1.11491 – ident: 823_CR50 – ident: 823_CR22 doi: 10.1109/CVPR.2016.90 – ident: 823_CR52 doi: 10.1145/3447548.3467401 – volume: 25 start-page: 24 year: 2019 ident: 823_CR15 publication-title: Nat Med doi: 10.1038/s41591-018-0316-z – volume: 12 start-page: 231 issue: 3 year: 1997 ident: 823_CR46 publication-title: Knowl Eng Rev doi: 10.1017/S0269888997003123 – volume: 29 start-page: 400 issue: 2 year: 2014 ident: 823_CR2 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-014-0349-y – volume: 73 start-page: 202 year: 2018 ident: 823_CR48 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2017.08.016 – ident: 823_CR19 – ident: 823_CR36 – ident: 823_CR32 – volume: 1 start-page: 206 year: 2019 ident: 823_CR39 publication-title: Nat Mach Intell doi: 10.1038/s42256-019-0048-x – ident: 823_CR12 doi: 10.1145/3359786 – ident: 823_CR37 doi: 10.1145/2939672.2939778 – ident: 823_CR53 – ident: 823_CR16 doi: 10.1145/3292500.3330712 – volume: 8 start-page: 1341 issue: 7 year: 1996 ident: 823_CR51 publication-title: Neural Comput doi: 10.1162/neco.1996.8.7.1341 – volume: 5 start-page: 197 year: 1990 ident: 823_CR42 publication-title: Mach Learn – volume: 7 start-page: 1 year: 2006 ident: 823_CR10 publication-title: J Mach Learn Res |
| SSID | ssj0005230 |
| Score | 2.4904912 |
| Snippet | We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines... |
| SourceID | hal proquest crossref springer |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 917 |
| SubjectTerms | Artificial Intelligence Chemistry and Earth Sciences Classification Classifiers Computer Science Data collection Data Mining and Knowledge Discovery Information Storage and Retrieval Machine learning Missing data Multivariate analysis Physics Statistics for Engineering Time series |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fS8MwED7cfPHF3-L8RRDfNNg2SZv6IlMmQ9wQcTCfSptm-KBzuin633uXpU4FfW3SFnJ3yZfku_sADlId6ygdKJ5GpeFS5hhzwSDkSunABmUuYqex1OnG7Z687Ku-P3Abe1plNSe6ibp8MnRGfowLCWJ5xNfh6eiZk2oU3a56CY0azOMUrHUd5s9a3eubbyQPMc0T1pIrHQY-bcYnz8Wh5sRmd9dNPP6xNNXuiRj5DXX-uih168_FMix64MiaU0uvwJwdrsJSJcrAfIyuwV2_1TlhzSGz76MHnxrFiw9eOqoGw12rfcRHbCodzRCzMkcqfMNNM-JORmLzjPzSjpkhaE1cIme-dehdtG7P29zrJ3AjlJhwDLXQUqZ0UlKmTpFbYXRSGj0oolIqI3B_KvLAijwtDOWKJPlAYIuOrSyMLMQG1IdPQ7sJDEMfoUdilRSJLBKrI2lSKYUxRhlEjA0Iq6HLjC8uThoXD9msLDINd4bDnbnhzuIGHH69M5qW1vi39z5a5KsjVcVuN68yekZVCNMolG9hA3Yqg2U-FsfZzHMacFQZcdb89y-3_v_aNixEznuI_bgD9cnLq91FhDIp9rwbfgKsmt5Q priority: 102 providerName: ProQuest |
| Title | XEM: An explainable-by-design ensemble method for multivariate time series classification |
| URI | https://link.springer.com/article/10.1007/s10618-022-00823-6 https://www.proquest.com/docview/2664955481 https://inria.hal.science/hal-03599214 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED7chuCLv8X5YwTxTQNtk7Spb1W6DXUi4mA-lTbN8EGn2Dn0v_eStXOKCj4VkrSFu1z7Hfm-O4DDUPrSC4eChl6uKOcpxpwzdKkQ0tFOnjLf9ljqXfndPj8fiEEpCisqtnt1JGm_1HNiN9-V1LDP7fEQ9WvQEJi7GyJX34vmiB1sqg2WnArpOqVU5udnfPkd1e4NGXIOaX47HLX_nPYqLJdgkURT767Bgh6tw0rViIGUcbkOi5bHqYoNuBvEvRMSjYh-e34ohVE0e6e5JWoQzFn1Iw6RaeNogoiVWErhBFNmRJ3EtJonZlfqgigDrA2TyDpvE_rt-PasS8vuCVQxwcYUA83VRicd5Eank6WaKRnkSg4zL-dCMcxOWepoloaZMkqRIB0ynJG-5pniGduC-uhppLeBYOAj8Ai04CzgWaClx1XIOVNKCYV4sQluZcRElaXFTYeLh-SzKLIxfIKGT6zhE78JR7N7nqeFNf5cfYC-mS00NbG70WVixkwNwtBz-cRtwl7luqSMxCJBAII5IOZlOH1cufNz-vdX7vxv-S4seXZfGS7kHtTHL696H_HKOGtBTbY7LWhEnbuLGK-n8dX1Tctu2g_k3uDK |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB7xOJQLjxbE8rSq9tRaTWI7cSohtGpZlrLLCaTl5CaOVxxgWdjl9af6G5lxEpYiwY2rnTjSzNjzTTzfDMCXVMc6SvuKp1FhuZQZ7rmgH3KldOCCIhOx77HUPYrbJ_JPT_Wm4F_NhaG0yvpM9Ad1cWnpH_kPdCSI5RFfh7vDK05do-h2tW6hUZrFoXu4w5BttHPwG_X7NYpae8e_2rzqKsCtUGLM0QBDR_zhpCD-Sp45YXVSWN3Po0IqKzBqE1ngRJbmlhgUSdYXOKNjJ3Mrc4HrTsOsFOjJiZne2n-WUiJKVrKWXOkwqEg6FVUvDjWn3Hl_ucXj_xzh9BmlYT7DuC-uZb23ay3CfAVTWbO0qyWYcoOPsFC3gGDVifAJTnt73Z-sOWDufnheEbF4_sALnxjCMEZ2FzjEykbVDBEy8ymMtxiiI8pl1Nqe0S5wI2YJyFPmkjeWZTh5F7muwMzgcuBWgeFBg0AncUqKROaJ05G0qZTCWqss4tMGhLXojK1KmVNHjXMzKcJM4jYobuPFbeIGfHt6Z1gW8njz6c-okacHqQZ3u9kxNEY1D9MolLdhAzZqhZlq54_MxE4b8L1W4mT69U-uvb3aNnxoH3c7pnNwdLgOc5G3JMq73ICZ8fWN20RsNM63vEEy-PveO-ARL0oZcw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwED9tnYR4gfElCtuwEDyBtSS2Ewdpmgpr1bGtmhCTypOXOI54GF2hZR__Gn_d7hxnHUjsba924kh35_Pv4t_dAbzJdaqTvFY8TyrLpSxwz0V1zJXSkYuqQqS-x9LBKB0eyc9jNV6CP20uDNEqW5_oHXV1aukf-SYeJIjlEV_Hm3WgRRzuDLanPzl1kKKb1radRmMie-7yHMO32dbuDur6bZIM-l8_DXnoMMCtUGLO0RhjR7nEWUW5LGXhhNVZZXVdJpVUVmAEJ4rIiSIvLWVTZEUtcEanTpZWlgLXXYaVjKKiDqx87I8Ov9wgmIgmR1lLrnQchZSdkLiXxpoTk95fdfH0r2Nx-TuRMm8g3n8uaf3ZN1iFBwG0sl5jZY9gyU0ew8O2IQQL_uEJfBv3Dz6w3oS5i-lJSMvi5SWvPE2EYcTsfuAQa9pWM8TLzBMazzBgR8zLqNE9oz3hZswSrCcekzedp3B0J5J9Bp3J6cQ9B4ZuB2FP5pQUmSwzpxNpcymFtVZZRKtdiFvRGRsKm1N_jROzKMlM4jYobuPFbdIuvLt-Z9qU9bj16deokesHqSL3sLdvaIwqIOZJLM_iLqy1CjPBD8zMwmq78L5V4mL6_598cftqr-AeWr_Z3x3tvYT7iTckImGuQWf-67dbR6A0LzeCRTI4vutNcAVNtB8F |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=XEM%3A+An+explainable-by-design+ensemble+method+for+multivariate+time+series+classification&rft.jtitle=Data+mining+and+knowledge+discovery&rft.au=Fauvel%2C+Kevin&rft.au=Fromont%2C+%C3%89lisa&rft.au=Masson%2C+V%C3%A9ronique&rft.au=Faverdin%2C+Philippe&rft.date=2022-05-01&rft.issn=1384-5810&rft.eissn=1573-756X&rft.volume=36&rft.issue=3&rft.spage=917&rft.epage=957&rft_id=info:doi/10.1007%2Fs10618-022-00823-6&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10618_022_00823_6 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1384-5810&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1384-5810&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1384-5810&client=summon |