Computer Science ›› 2017, Vol. 44 ›› Issue (1): 37-41.doi: 10.11896/j.issn.1002-137X.2017.01.007

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Online Sequential Active Learning Approach

ZHAI Jun-hai, ZANG Li-guang and ZHANG Su-fang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In the real world,there are a lot of unlabelled data,such as various medical images and web data,etc.In the era of big data,this situation is more prominent.It is expensive to label large amount of unlabelled data.Active learning is an effective method to solve this problem,and it is one of the hot research topics in the field of machine learning and data mining.Based on online sequential extreme learning machine,an active learning algorithm was proposed in this paper.Due to the nature of incremental learning embedded in online sequential extreme learning machine,the proposed algorithm can significantly improve the efficiency of learning system.Furthermore,the proposed algorithm uses instance entropy as heuristic to measure the importance of the unlabeled instances,and uses K-nearest neighbor classifier as Oracle to label the selected instances.The experimental results show that the proposed algorithm has fast learning speed with exact labeling.

Key words: Active learning,Extreme learning machine,Online sequential learning,Instance entropy,K-nearest neighbors

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