Share Email Print
cover

Proceedings Paper

An active MBBNTree classifier learning from unlabeled samples
Author(s): Yong C. Cao; Yue Zhao; Xiu Q. Pan; Yong Lu; Xiao N. Xu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Obtaining labeled training examples for some classification tasks is often expensive, such as text classification, mail filtering, while gathering large quantities of unlabeled examples is usually very cheap. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. MBBNTree algorithm, which integrates the advantage of Markov Blanket Bayesian Networks (MBBN) and Decision Tree, would behave better performance than other Bayesian Networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. In this paper, the MBBNTree classifier algorithm based on the Query-by-Committee of active learning would be presented to solve the problem of learning MBBNTree classifier from unlabeled samples. Experimental results show that the proposed algorithm can reach the same accuracy as passive learning with few labeled training examples.

Paper Details

Date Published: 13 October 2008
PDF: 7 pages
Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 71281D (13 October 2008); doi: 10.1117/12.806669
Show Author Affiliations
Yong C. Cao, Central Univ. for Nationalities (China)
Yue Zhao, Central Univ. for Nationalities (China)
Xiu Q. Pan, Central Univ. for Nationalities (China)
Yong Lu, Central Univ. for Nationalities (China)
Xiao N. Xu, Central Univ. for Nationalities (China)


Published in SPIE Proceedings Vol. 7128:
Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment

© SPIE. Terms of Use
Back to Top