Rough Sets International Joint Conference, IJCRS 2021, Bratislava, Slovakia, September 19-24, 2021, Proceedings

• Main Authors: Ramanna, Sheela, Cornelis, Chris, Ciucci, Davide
• Format: eBook Conference Proceeding
• Language: English
• Published: Cham Springer Nature 2021; Springer International Publishing AG; Springer International Publishing
• Edition: 1
• Series: Lecture Notes in Computer Science
• Subjects: Artificial Intelligence; Computer Applications; Computer Science; Data Mining and Knowledge Discovery; Data processing Computer science; Information Systems Applications (incl. Internet); Rough sets

The volume LNAI 12872 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2021, Bratislava, Slovak Republic, in September 2021. The conference was held as a hybrid event due to the COVID-19 pandemic.The 13 full paper and 7 short papers presented were carefully revi...

Table of Contents:

• An Opinion Summarization-Evaluation System Based on Pre-trained Models -- 1 Introduction -- 2 Related Works -- 3 An Opinion Summarization-Evaluation Algorithm -- 3.1 Subjective Analysis and Opinion Mining -- 3.2 Hierarchical Metrics -- 4 Experiments and Analysis -- 4.1 Experimental Settings -- 4.2 Experiment Results and Analysis -- 5 Conclusion and Future Works -- References -- Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Cleaning -- 3.2 Tweet Embedding -- 3.3 Similarity Relation -- 3.4 Classification Methods -- 3.5 Evaluation Method -- 4 Experiments -- 4.1 Detecting the Best Setup for Embeddings -- 4.2 Ensembles -- 5 Results on the Test Data -- 6 Conclusion and Future Work -- References -- Three-Way Decisions Based RNN Models for Sentiment Classification -- 1 Introduction -- 2 Related Work -- 2.1 RNN Models -- 2.2 Three-Way Decisions -- 3 The Proposed Method -- 3.1 Algorithm -- 3.2 Probability Adjustment Strategies -- 4 Experiment -- 4.1 Datasets and Baseline Methods -- 4.2 Experimental Results -- 4.3 Parameter Analysis -- 5 Conclusion -- References -- Tolerance-Based Short Text Sentiment Classifier -- 1 Introduction -- 2 Data Sets -- 3 Models -- 3.1 Tolerance Near Sets -- 3.2 Transformer Model -- 4 Experiments and Analysis of Results -- 5 Conclusion -- References -- Knowledge Graph Representation Learning for Link Prediction with Three-Way Decisions -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Graph Embedding Models -- 2.2 Three-Way Decisions -- 3 Our Approach -- 3.1 Relation Neighbor -- 3.2 Knowledge Representation with Three-Way Decisions -- 3.3 Loss Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines and Experiment Setting -- 4.3 Evaluation Metrics -- 4.4 Experiment Results -- 5 Conclusion -- References
• 2.4 Closure Operators -- 2.5 Relationships on Attribute Sets -- 3 Functional Dependency Relations -- 4 Conclusions -- References -- The RSDS: A Current State and Future Plans -- 1 Generally About the RSDS -- 2 Further Plans -- 3 Final Remarks -- References -- Many-Valued Dynamic Object-Oriented Inheritance and Approximations -- 1 Introduction -- 2 Preliminaries -- 2.1 Many-Valued Logics -- 2.2 Nested Structures -- 2.3 Rule-Based Object-Oriented Query Languages -- 3 Many-Valued Dynamic Object Inheritance -- 4 Approximations -- 5 Conclusions -- References -- Related Methods and Hybridization -- Minimizing Depth of Decision Trees with Hypotheses -- 1 Introduction -- 2 Decision Tables -- 3 Decision Trees -- 4 Construction of Directed Acyclic Graph (T) -- 5 Minimizing the Depth of Decision Trees -- 6 Results of Experiments -- 7 Conclusions -- References -- The Influence of Fuzzy Expectations on Triples of Triangular Norms in the Weighted Fuzzy Petri Net for the Subject Area of Passenger Transport Logistics -- 1 Introduction -- 2 Fuzzy Expectations for wFPN -- 3 The Review of wFPN Model for the Experiment on Triples of Functions -- 4 The Influence of Fuzzy Expectations on the Results of the wFPN Model -- 5 Conclusions -- References -- Possibility Distributions Generated by Intuitionistic L-Fuzzy Sets -- 1 Introduction -- 2 Preliminaries -- 2.1 Algebraic Structures of Truth Values -- 2.2 Intuitionistic Fuzzy Sets and Intuitionistic L-fuzzy Sets -- 3 From Intuitionistic L-Fuzzy Sets to Possibility Distributions -- 3.1 Possibility Distributions -- 3.2 Possibility Distributions Generated by Intuitionistic L-fuzzy Sets -- 3.3 Possibility Distributions Generated by Intuitionistic L-fuzzy Sets Based on an IMTL-algebra -- 4 From Possibility Distributions to Intuitionistic Fuzzy Sets
• PNeS in Modelling, Control and Analysis of Concurrent Systems
• 4.1 An Algorithm to Find the Intuitionistic L-fuzzy Set Generating a Given Possibility Distribution -- 5 Conclusions and Future Directions -- References -- Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets -- 1 Introduction -- 2 Background -- 2.1 Possibility Theory -- 2.2 Rough Set Theory -- 2.3 Belief Function Theory -- 3 Possibilistic Decision Tables and Reducts -- 3.1 Possibilistic Decision Tables -- 3.2 Possibilistic Reducts -- 3.3 Entropy Reducts -- 4 Conclusion -- References -- Right Adjoint Algebras Versus Operator Left Residuated Posets -- 1 Introduction -- 2 Preliminaries -- 2.1 Dedekind-MacNeille Completion -- 2.2 Algebraic Structures -- 3 Adjoint Property in Operator Left Residuated Posets -- 3.1 Extension of M and R to 2P -- 3.2 Requirements for a Proper Fuzzy Modus Ponens -- 3.3 Extension of Operator Left Residuated Posets -- 4 Operator Left Residuated Posets from a Dedekind-MacNeille Completion -- 5 Conclusions and Future Work -- References -- Adapting Fuzzy Rough Setspg for Classification with Missing Values -- 1 Introduction -- 2 Interval-Valued Fuzzy Rough Sets -- 3 FRNN with Interval-Valued Approximations -- 4 Conclusion -- References -- Areas of Applications -- Spark Accelerated Implementation of Parallel Attribute Reduction from Incomplete Data -- 1 Introduction -- 2 Preliminaries -- 2.1 Apache Spark Computing Model -- 3 Spark Parallelization of Attribute Reduction from Incomplete Data -- 4 Experimental Evaluation -- 4.1 Selection of the Number of Data Partitions -- 4.2 Evaluation of the Parallelism Metrics -- 5 Conclusions -- References -- Attention Enhanced Hierarchical Feature Representation for Three-Way Decision Boundary Processing -- 1 Introduction -- 2 Proposed Method -- 3 Performance Evaluation -- 4 Conclusion -- References
• 2.1 Seismic Noise Reduction Methods -- 2.2 Noise Modeling Based Denoising Methods -- 2.3 AutoEncoder Based Denoising Methods -- 3 DDAE-GAN Based Blind Denoiser -- 3.1 Paried Data Constructing -- 3.2 Pre-training -- 3.3 Transfer Learning -- 4 Examples -- 4.1 Synthetic Examples -- 4.2 Field Examples -- 5 Conclusions -- References -- Classification of Multi-class Imbalanced Data: Data Difficulty Factors and Selected Methods for Improving Classifiers -- 1 Introduction -- 2 Related Works on Classification of Multi-class Imbalanced Data -- 3 Difficulty Factors in Imbalanced Data -- 3.1 Earlier Studies on Binary Imbalanced Classes -- 3.2 Multi-class Difficulties -- 4 Identifying Types of Examples in Multi-class Imbalanced Data -- 5 Discovering Split of Classes into Sub-concepts and Rare Examples -- 6 Multi-class Hybrid Resampling Algorithm SOUP -- 7 Multi-class Variant of BRACID Algorithm -- 7.1 Rule Induction from Binary Imbalanced Data with BRACID -- 7.2 Generalizing BRACID for Multiple Imbalanced Classes -- 8 Multi-class Extension of Bagging Ensemble -- 9 Software Implementations of Specialized Algorithms for Multi-class Imbalanced Data -- 10 Future Research Directions and Conclusions -- References -- Core Rough Set Models and Methods -- General Rough Modeling of Cluster Analysis -- 1 Introduction -- 1.1 Background -- 2 New Rough Semantic Approaches -- References -- Possible Coverings in Incomplete Information Tables with Similarity of Values -- 1 Introduction -- 2 Rough Sets from Coverings in Complete Information Tables -- 3 Rough Sets from Possible Coverings in Incomplete Information Tables -- 4 Conclusions -- References -- Attribute Reduction Using Functional Dependency Relations in Rough Set Theory -- 1 Introduction -- 2 Preliminaries -- 2.1 Rough Sets -- 2.2 Reducts for Information Systems -- 2.3 Functional Dependency Relations
• Intro -- Preface -- Organization -- Contents -- Invited Papers -- Mining Incomplete Data Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets and Maximal Consistent Blocks -- 1 Introduction -- 2 Incomplete Data -- 3 Probabilistic Approximations -- 3.1 Global Probabilistic Approximations Based on Characteristic Sets -- 3.2 Saturated Probabilistic Approximations Based on Characteristic Sets -- 3.3 Global Probabilistic Approximations Based on Maximal Consistent Blocks -- 3.4 Saturated Probabilistic Approximations Based on Maximal Consistent Blocks -- 3.5 Rule Induction -- 4 Experiments -- 5 Conclusions -- References -- Determining Tanimoto Similarity Neighborhoods of Real-Valued Vectors by Means of the Triangle Inequality and Bounds on Lengths -- 1 Introduction -- 2 Basic Notions and Properties -- 2.1 The Euclidean Distance, the Cosine Similarity and the Tanimoto Similarity -- 2.2 ε-Neighborhoods and k Nearest Neighbors -- 3 Using the Triangle Inequality Property to Calculate Euclidean and Cosine ε-Neighborhoods -- 3.1 Using the Triangle Inequality to Calculate Euclidean ε-Neighborhoods -- 3.2 Calculating Cosine ε-Neighborhoods by Means of the Triangle Inequality -- 4 Using Bounds on Vector Lengths to Calculate Tanimoto Similarity ε-Neighborhoods -- 5 Calculating Tanimoto ε-Neighborhoods by Means of the Triangle Inequality -- 6 Calculating Tanimoto ε-Neighborhoods by Means of the Triangle Inequality and Lengths of Vectors -- 7 Summary -- References -- Rough-Fuzzy Segmentation of Brain MR Volumes: Applications in Tumor Detection and Malignancy Assessment -- 1 Introduction -- 2 Segmentation of Brain MR Images -- 3 Brain Tumor Detection and Gradation -- References -- DDAE-GAN: Seismic Data Denoising by Integrating Autoencoder and Generative Adversarial Network -- 1 Introduction -- 2 Related Work