NDLI: An active TAN classifier based on vote entropy-maximum entropy of QBC

Content Provider	IEEE Xplore Digital Library
Author	Zhao, Y. Cao, Y.C.
Copyright Year	2009
Description	Author affiliation: School of Information and Engineering, Minzu University of China (Zhao, Y.; Cao, Y.C.)
Abstract	Tree-Augmented Naïve Bayes (TAN) is a state-of-the-art extension of the naive Bayes, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that are characteristic of naive Bayes. But TAN classifier was built by the conventional passive learning. The available training samples with actual classes are not enough for passive learning method for modeling TAN classifier in practice. The Query-by-Committee (QBC) method of active learning can examine unlabelled examples and selects only those that are most informative for labeling. It aims at using few labeled training examples to build efficient classifier. In this paper, an active TAN classifier algorithm based on vote entropy-maximum entropy of QBC is presented to solve the problem of building TAN classifier from unlabelled samples. Experimental results show that the proposed algorithm can reach the same accuracy as passive learning with few labeled training examples.
Starting Page	1587
Ending Page	1590
File Size	123576
Page Count	4
File Format	PDF
ISBN	9781424447060
DOI	10.1109/ICCA.2009.5410440
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-09
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Voting Entropy Classification tree analysis Bayesian methods Automatic control Automation Robustness Learning systems Labeling Sampling methods
Content Type	Text
Resource Type	Article

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