Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy
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[1] Rui Liu,et al. Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification , 2019, Inf. Sci..
[2] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[3] Francisco Herrera,et al. SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary , 2018, J. Artif. Intell. Res..
[4] Yunchuan Sun,et al. Spectral–Spatial HyperspectralImage Classification With K-Nearest Neighbor and Guided Filter , 2018, IEEE Access.
[5] Jiye Liang,et al. A multi-view OVA model based on decision tree for multi-classification tasks , 2017, Knowl. Based Syst..
[6] Gee Wah Ng,et al. Classification for overlapping classes using optimized overlapping region detection and soft decision , 2010, 2010 13th International Conference on Information Fusion.
[7] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[8] Cheng-Lin Liu. Partial discriminative training for classification of overlapping classes in document analysis , 2008, International Journal of Document Analysis and Recognition (IJDAR).
[9] Mohamed Abdelrazek,et al. An Ensemble Oversampling Model for Class Imbalance Problem in Software Defect Prediction , 2018, IEEE Access.
[10] Ryan M. Rifkin,et al. In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..
[11] Gee Wah Ng,et al. Managing Category Proliferation in Fuzzy ARTMAP Caused by Overlapping Classes , 2009, IEEE Transactions on Neural Networks.
[12] Longsheng Cheng,et al. CLASSIFICATION OF CLASS OVERLAPPING DATASETS BY KERNEL-MTS METHOD , 2017 .
[13] Haitao Xiong,et al. Classification Algorithm based on NB for Class Overlapping Problem , 2013 .
[14] Chao Zhang,et al. Binary Output Layer of Feedforward Neural Networks for Solving Multi-Class Classification Problems , 2019, IEEE Access.
[15] Khaled Elleithy,et al. Android Malware Permission-Based Multi-Class Classification Using Extremely Randomized Trees , 2018, IEEE Access.
[16] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[17] Sotiris B. Kotsiantis,et al. Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems , 2017, EANN.
[18] Francisco Herrera,et al. Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition , 2012, Knowledge and Information Systems.
[19] Luc Devroye,et al. Lectures on the Nearest Neighbor Method , 2015 .
[20] Chih-Jen Lin,et al. Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..
[21] Jian Chu,et al. A novel SVM modeling approach for highly imbalanced and overlapping classification , 2011, Intell. Data Anal..
[22] José Salvador Sánchez,et al. An Empirical Study of the Behavior of Classifiers on Imbalanced and Overlapped Data Sets , 2007, CIARP.
[23] Jacek Tabor,et al. Two ellipsoid Support Vector Machines , 2014, Expert Syst. Appl..
[24] Francisco Herrera,et al. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes , 2011, Pattern Recognit..
[25] André Carlos Ponce de Leon Ferreira de Carvalho,et al. A review on the combination of binary classifiers in multiclass problems , 2008, Artificial Intelligence Review.
[26] Francisco Herrera,et al. Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure , 2016, Neurocomputing.
[27] Daqi Gao,et al. Classification for Imbalanced and Overlapping Classes Using Outlier Detection and Sampling Techniques , 2013 .
[28] Gustavo E. A. P. A. Batista,et al. Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior , 2004, MICAI.
[29] Gustavo E. A. P. A. Batista,et al. Balancing Strategies and Class Overlapping , 2005, IDA.
[30] Kaddour Sadouni,et al. Binary tree multi-class SVM based on OVA approach and variable neighbourhood search algorithm , 2017, Int. J. Comput. Appl. Technol..
[31] Yanping Zhang,et al. A Parameter-Free Cleaning Method for SMOTE in Imbalanced Classification , 2019, IEEE Access.
[32] Eyke Hüllermeier,et al. Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting , 2010, Pattern Recognit..
[33] Francisco Herrera,et al. Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme , 2017, Knowl. Based Syst..
[34] Ligang Zhou,et al. One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies , 2017, Inf. Fusion.
[35] Nicolaos B. Karayiannis,et al. Handling class overlap with variance-controlled neural networks , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[36] Fang Wu,et al. Step-wise support vector machines for classification of overlapping samples , 2015, Neurocomputing.
[37] Jerzy Stefanowski,et al. Dealing with Data Difficulty Factors While Learning from Imbalanced Data , 2016, Challenges in Computational Statistics and Data Mining.
[38] Chidchanok Lursinsap,et al. Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms , 2015, Neurocomputing.
[39] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[40] Xiaofeng Zhu,et al. Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[41] José Martínez Sotoca,et al. When Overlapping Unexpectedly Alters the Class Imbalance Effects , 2007, IbPRIA.
[42] Emilio Corchado,et al. A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.
[43] Rahul Khanna,et al. Support Vector Machines for Classification , 2015 .
[44] Johannes Fürnkranz,et al. Round Robin Classification , 2002, J. Mach. Learn. Res..
[45] Tony R. Martinez,et al. Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..