面向类别不平衡数据的主动在线加权极限学习机算法

摘要

It is well known that most existing active learning algorithms often fail to provide excellent performance and cost much training time when they are used in the scenario of class imbalance.To deal with this problem,a hybrid active learning algorithm named AOW-ELM algorithm was proposed.The algorithm uses ELM (extreme learning machine)which has rapid modeling speed as base classifier in active learning.In addition,weighted ELM algorithm is adopted to guarantee the impartiality in the procedure of active learning.Next,to further accelerate the process of active learning,i.e.,decreasing the time consumption of active learning,online learning procedure of weighted ELM algorithm was deduced in theory.Experimental results on 12 baseline binary-class imbalanced data sets indicate the effectiveness and feasibility of the proposed algorithm.%针对在样本类别分布不平衡场景下,现有的主动学习算法普遍失效及训练时间过长等问题,提出采用建模速度更快的极限学习机,即ELM(Extreme Learning Machine)作为主动学习的基分类器,并以加权ELM算法用于主动学习过程的平衡控制,进而在理论上推导了其在线学习的过程,大幅降低了主动学习的时间开销,并将最终的混合算法命名为AOW-ELM算法.通过12个基准的二类不平衡数据集验证了该算法的有效性与可行性.