An active learning paradigm based on a priori data reduction and organization
•A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user in...
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Published in | Expert systems with applications Vol. 41; no. 14; pp. 6086 - 6097 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier Ltd
15.10.2014
Elsevier |
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Abstract | •A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user interaction.•Results are shown with different clustering and classification strategies, and on a variety of real-world datasets.
In the past few years, active learning has been reasonably successful and it has drawn a lot of attention. However, recent active learning methods have focused on strategies in which a large unlabeled dataset has to be reprocessed at each learning iteration. As the datasets grow, these strategies become inefficient or even a tremendous computational challenge. In order to address these issues, we propose an effective and efficient active learning paradigm which attains a significant reduction in the size of the learning set by applying an a priori process of identification and organization of a small relevant subset. Furthermore, the concomitant classification and selection processes enable the classification of a very small number of samples, while selecting the informative ones. Experimental results showed that the proposed paradigm allows to achieve high accuracy quickly with minimum user interaction, further improving its efficiency. |
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AbstractList | •A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user interaction.•Results are shown with different clustering and classification strategies, and on a variety of real-world datasets.
In the past few years, active learning has been reasonably successful and it has drawn a lot of attention. However, recent active learning methods have focused on strategies in which a large unlabeled dataset has to be reprocessed at each learning iteration. As the datasets grow, these strategies become inefficient or even a tremendous computational challenge. In order to address these issues, we propose an effective and efficient active learning paradigm which attains a significant reduction in the size of the learning set by applying an a priori process of identification and organization of a small relevant subset. Furthermore, the concomitant classification and selection processes enable the classification of a very small number of samples, while selecting the informative ones. Experimental results showed that the proposed paradigm allows to achieve high accuracy quickly with minimum user interaction, further improving its efficiency. In the past few years, active learning has been reasonably successful and it has drawn a lot of attention. However, recent active learning methods have focused on strategies in which a large unlabeled dataset has to be reprocessed at each learning iteration. As the datasets grow, these strategies become inefficient or even a tremendous computational challenge. In order to address these issues, we propose an effective and efficient active learning paradigm which attains a significant reduction in the size of the learning set by applying an a priori process of identification and organization of a small relevant subset. Furthermore, the concomitant classification and selection processes enable the classification of a very small number of samples, while selecting the informative ones. Experimental results showed that the proposed paradigm allows to achieve high accuracy quickly with minimum user interaction, further improving its efficiency. |
Author | de Rezende, Pedro J. Suzuki, Celso T.N. Saito, Priscila T.M. Gomes, Jancarlo F. Falcão, Alexandre X. |
Author_xml | – sequence: 1 givenname: Priscila T.M. surname: Saito fullname: Saito, Priscila T.M. email: maeda@ic.unicamp.br organization: Institute of Computing, University of Campinas, SP, Brazil – sequence: 2 givenname: Pedro J. surname: de Rezende fullname: de Rezende, Pedro J. email: rezende@ic.unicamp.br organization: Institute of Computing, University of Campinas, SP, Brazil – sequence: 3 givenname: Alexandre X. surname: Falcão fullname: Falcão, Alexandre X. email: afalcao@ic.unicamp.br organization: Institute of Computing, University of Campinas, SP, Brazil – sequence: 4 givenname: Celso T.N. surname: Suzuki fullname: Suzuki, Celso T.N. email: celso.suzuki@ic.unicamp.br organization: Institute of Computing, University of Campinas, SP, Brazil – sequence: 5 givenname: Jancarlo F. surname: Gomes fullname: Gomes, Jancarlo F. email: jgomes@ic.unicamp.br organization: Institute of Computing, University of Campinas, SP, Brazil |
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CitedBy_id | crossref_primary_10_1016_j_eswa_2014_09_038 crossref_primary_10_1186_s12982_023_00120_7 crossref_primary_10_1016_j_asoc_2016_05_041 crossref_primary_10_3390_computers5010001 crossref_primary_10_1016_j_patcog_2015_05_020 crossref_primary_10_1016_j_asoc_2016_06_008 crossref_primary_10_1016_j_cmpb_2022_107122 crossref_primary_10_1016_j_ins_2017_08_012 crossref_primary_10_1109_TIE_2020_2969106 |
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Keywords | Pattern recognition Data mining Active learning Image annotation Machine learning Image processing High precision Very large databases Active system Efficiency User interface Classification Learning algorithm Small medium sized firm Data analysis Process selection Data reduction Interactive system Dimension reduction Experimental result Supervised learning Learning (artificial intelligence) Small sample Artificial intelligence Indexing |
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Snippet | •A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of... In the past few years, active learning has been reasonably successful and it has drawn a lot of attention. However, recent active learning methods have focused... |
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SubjectTerms | Active learning Applied sciences Artificial intelligence Classification Computation Computer science; control theory; systems Data mining Data processing. List processing. Character string processing Data reduction Exact sciences and technology Expert systems Image annotation Information systems. Data bases Iterative methods Learning Machine learning Memory organisation. Data processing Organizations Pattern recognition Pattern recognition. Digital image processing. Computational geometry Software Strategy |
Title | An active learning paradigm based on a priori data reduction and organization |
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