Recent advances in active learning algorithms

doi:10.3969/j.issn.1000-1565.2017.02.017

Abstract

Abstract: Active learning mainly aims at reducing the cost of manual annotation without decreasing the accuracy of the classifier.Active learning algorithm gets high quality training sample set by selecting the informative unlabeled samples which are labeled by domain experts later.The selected sample set is used to train the classifier.This improves the generalization ability of trained classifier while minimizes the cost of the labeling.Firstly,the recent advances in the three key steps in active learning algorithm was summarized,including:1)the method for constructing the initial training sample set and its improvement;2)the sample selection strategy and its improvement;3)the termination condition and its improvement.Then,the problems in active learning were analyzed and the corresponding countermeasures were presented.Finally,the future works in active learning were addressed.

Key words: active learning, initial training sample set, sample selection strategy, termination condition

CLC Number:

TP181

YANG Wenzhu,TIAN Xiaoxiao,WANG Sile,ZHANG Xizhong. Recent advances in active learning algorithms[J]. Journal of Hebei University (Natural Science Edition), 2017, 37(2): 216-224.

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