Computer Science ›› 2017, Vol. 44 ›› Issue (12): 221-226.doi: 10.11896/j.issn.1002-137X.2017.12.040

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Active,Online and Weighted Extreme Learning Machine Algorithm for Class Imbalance Data

WANG Chang-bao, LI Qing-wen and YU Hua-long   

  • Online:2018-12-01 Published:2018-12-01

Abstract: 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.

Key words: Active learning,Class imbalance learning,Extreme learning machine,Weighted extreme learning machine,Online learning

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