A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Boosting for Learning Multiple Classes with Imbalanced Class Distribution
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
IEEE Transactions on Knowledge and Data Engineering
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Relationships between Diversity of Classification Ensembles and Single-Class Performance Measures
IEEE Transactions on Knowledge and Data Engineering
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Processing imbalanced data sets has become a challenging issue in machine learning and data mining communities. Although many researches in the literature have focused on two class problems, multiclass problems have attracted a lot of attention recently. Many existing solutions for multiclass tasks are focused on class decomposition methods, i.e. divide the problem into some two-class sub-problems which are easier to handle. This paper presents a Genetic Algorithm-Based Ensemble Pruning Algorithm, called GAB-EPA, for multiclass imbalanced problems without applying any class decomposition techniques. In effect, GAB-EPA seeks to find the best subset of classifiers that not only are accurate in their predictions, but also can generate an admissible diversity when gather together as an ensemble model. To show the effectiveness of our approach, we compared our results with other popular ensemble algorithms in terms of three evaluation metrics: Minority Class Recall, G-mean, and MAUC.