Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data
Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data
复制标题
通过集成学习对多类不平衡数据进行一对一分解
DOI：
10.1016/j.knosys.2016.05.048
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发表时间：
2016-08-15
影响因子：
8.8
通讯作者：
Herrera, Francisco
中科院分区：
文献类型：
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作者：
Zhang, Zhongliang;Krawczyk, Bartosz;Herrera, Francisco
多类不平衡分类问题出现在许多实际应用中，这些应用存在类别分布差异很大的情况。分解策略是解决涉及多类的分类问题的著名技术。其中，使用一对一和一对多的二元方法受到了研究界的极大关注。它们允许将多类问题分解为几个更容易解决的两类子问题。在这项研究中，我们进行了详尽的实证分析，以探索应用二元集成学习方法增强一对一方案用于多类不平衡分类问题的可能性。我们研究了几种为解决不平衡问题而提出的最先进的集成学习方法，以解决从多类数据集衍生出的成对任务。然后采用聚合策略来组合二元集成输出，以重建原始的多类任务。我们在统计分析的支持下，对所提出的方法进行了详细的实验研究。结果表明，一对一方案的集成学习在处理多类不平衡分类问题方面具有很高的有效性。(C) 2016爱思唯尔B.V. 保留所有权利。
Multi-class imbalance classification problems occur in many real-world applications, which suffer from the quite different distribution of classes. Decomposition strategies are well-known techniques to address the classification problems involving multiple classes. Among them binary approaches using one-vs-one and one-vs-all has gained a significant attention from the research community. They allow to divide multi-class problems into several easier-to-solve two-class sub-problems. In this study we develop an exhaustive empirical analysis to explore the possibility of empowering the one-vs-one scheme for multi class imbalance classification problems with applying binary ensemble learning approaches. We examine several state-of-the-art ensemble learning methods proposed for addressing the imbalance problems to solve the pairwise tasks derived from the multi-class data set. Then the aggregation strategy is employed to combine the binary ensemble outputs to reconstruct the original multi-class task. We present a detailed experimental study of the proposed approach, supported by the statistical analysis. The results indicate the high effectiveness of ensemble learning with one-vs-one scheme in dealing with the multi class imbalance classification problems. (C) 2016 Elsevier B.V. All rights reserved.