Abstract
Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling,RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show thatRotEasypossesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.
Highlights
Classification with imbalanced data sets has emerged as one of the most challenging tasks in data mining community
Experimental results indicate that our approach outperforms the compared state-of-the-art imbalanced learning methods significantly
Based on the above analysis, we propose a novel selective ensemble construction technique RotEasy, integrating feature extraction, and ensemble pruning with EasyEnsemble to further improve the ensemble diversity
Summary
Classification with imbalanced data sets has emerged as one of the most challenging tasks in data mining community. Class imbalance occurs when examples of one class are severely outnumbered by those of other classes. Traditional data mining algorithms tend to favor the overrepresented (majority or negative) class, resulting in unacceptably low recognition rates with respect to the underrepresented (minority or positive) class. The underrepresented minority class usually represents the positive concept with great interest than the majority class. The classification accuracy of the minority class is more preferred than the majority class. The recognition goal of medical diagnosis is to provide a higher identification accuracy for rare diseases. Similar to most of the existing imbalanced learning methods in the literature, we focus on two-class imbalanced classification problems in our current study
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