关键词: 极限学习机, 类不平衡学习, 模糊加权, 先验分布信息

Abstract:  Firstly, this paper analyzes the reason that the performance of extreme learning machine (ELM) is des-troyed by imbalanced instance distribution in theory. Then, based on the same theoretical framework, this paper discusses the effectiveness and inherent shortcomings of the weighted extreme learning machine (WELM). Nextly, profiting from the idea of fuzzy set, this paper proposes four fuzzy weighted extreme learning machine (FWELM) algorithms to deal with class imbalance problem. Finally, this paper verifies the effectiveness and feasibility of these four FWELM algorithms by the experiments constructing on 20 baseline binary-class imbalanced data sets. The exper-imental results indicate that the proposed algorithms can often acquire better classification performance than WELM algorithm and several traditional class imbalance learning algorithms in the context of ELM. In addition, in contrast with fuzzy support vector machine for class imbalance learning (FSVM-CIL) series algorithms, the proposed algorithms can produce the comparable classification performance, but always consume less training time.

Key words:  extreme learning machine, class imbalance learning, fuzzy weighting, prior distribution information