Abstract
Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
If the points are above the dashed line, it indicates that the dynamic selection effect on the corresponding index is satisfactory, i.e., the performance of the dynamic selection ensemble classifiers are better than original algorithms and vice versa
Other results for 12 classical imbalanced classification algorithms are over the dashed line except the above two classification algorithms, which indicate incorporating dynamic selection can promote predictive performance (MAvA, precision, and F-measure) for binary class datasets
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Data imbalance is ubiquitous and encountered in the field of classification problems. It occurs when the number of instances for different classes are significantly out of proportion. The minority classes with fewer instances usually contain the essential information, which has been observed in broad application areas, such as medical diagnosis [1,2,3,4,5,6], sentiment or image classification [7,8], fault identification [9,10], etc. Many typical classifiers may generate unsatisfactory results due to a concentration on global accuracy while ignoring the identification performance for minority samples
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