By Topic

Feature Selection for Classifying Data Stream Based on Maximum Entropy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yao-zong Liu ; Sch. of Comput., Nanjing Univ. of Sci. & Technol., Nanjing, China ; Yong-li Wang ; Wei Wei ; Hong Zhang

Feature select ion is an important problem in the fields of machine learning and pat tern recognition. Data stream data classification with high dimensional and sparse, and the dimension of the need for compression, feature selection methods suitable for data stream classification study of very value of this area is currently a lack of in-depth study. This paper summarizes the current data flow classification feature selection research, analysis of the characteristics of different methods. Based on the principle of maximum entropy, naive Bayes with the technology on the data stream tuple feature selection attributes, divided into two different subsets of the merits, so as to enhance the work of C4.5 classifier results, the experiment proved not only StreamMEFS classification of time-saving, but also to improve the quality of the classification.

Published in:

Pattern Recognition, 2009. CCPR 2009. Chinese Conference on

Date of Conference:

4-6 Nov. 2009