面向不平衡数据流的自适应加权在线超限学习机算法

一般的在线学习算法对不平衡数据流的分类识别会遇到较大困难,特别是当数据流发生概念漂移时,对其进行分类会变得更困难.文中提出面向不平衡数据流的自适应加权在线超限学习机算法,自动调整实时到达的训练样本的惩罚参数,达到在线学习不平衡数据流的目的.文中算法可以适用于不同偏斜程度的静态数据流的在线学习和发生概念漂移时数据流的在线学习.理论分析和在多个真实数据流上的实验表明文中算法的正确性和有效性.

Adaptive Weighted Online Extreme Learning Machine for Imbalance Data Steam

It is problematic to classify data stream with imblanced class distributions for general online learning algorithms, especially in case of concept drift. In this paper, an adaptive weighted online extreme learning machine(AWO-ELM) is developed for imbalance data stream. AWO-ELM is an online learning method and it alleviates the class imbalance problem in chunk-by-chunk learning. Instead of adopting fixed weights, an efficient weight selection strategy is proposed to obtain better classification performance, and thus it can be applied to the task of learning static data stream with different imbalance ratio and the task of online learning with concept drift. The theoretical analysis and experimental results of several real data stream show that AWO-ELM obtains comparable or better classification performance than competing methods.