DocumentCode
578157
Title
A new active learning strategy in nearest neighbor classifier
Author
Wei-Ran Song ; Cai, Yong-hua ; Wu, Bo ; Sun, Tao
Author_Institution
Coll. of Appl. Sci., Beijing Univ. of Technol., Beijing, China
Volume
2
fYear
2012
fDate
15-17 July 2012
Firstpage
729
Lastpage
734
Abstract
In this paper, we propose an active sample selection algorithm (SSME) based on maximum entropy criterion. By calculating the information entropy of the unlabeled samples, the algorithm can find the most informative samples from unlabeled data set. Comparative experiments with random selection algorithm are conducted on 10 real data sets. The results show the superiority of our proposed algorithm in terms of predictive accuracy and condensing rate.
Keywords
entropy; learning (artificial intelligence); pattern classification; SSME; active learning strategy; active sample selection algorithm; condensing rate; information entropy; maximum entropy criterion; nearest neighbor classifier; predictive accuracy; Abstracts; Glass; Heart; Iris; Active learning; Instance selection; Maximum entropy criterion; Nearest neighbor classifier;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location
Xian
ISSN
2160-133X
Print_ISBN
978-1-4673-1484-8
Type
conf
DOI
10.1109/ICMLC.2012.6359015
Filename
6359015
Link To Document