Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification

Authors:
Krikamol Muandet;Sanparith Marukatat;Cholwich Nattee
Affiliations:
School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand 12000;National Electronic and Computer Technology Center, National Science and Technology Development Agency, Pathum Thani, Thailand 12120;School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand 12000
Venue:
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Year:
2009

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Abstract

There has recently been a large effort in using unlabeled data in conjunction with labeled data in machine learning. Semi-supervised learning and active learning are two well-known techniques that exploit the unlabeled data in the learning process. In this work, the active learning is used to query a label for an unlabeled data on top of a semi-supervised classifier. This work focuses on the query selection criterion. The proposed criterion selects the example for which the label change results in the largest pertubation of other examples' label. Experimental results show the effectiveness of the proposed query selection criterion in comparison to existing techniques.