Paper Info
Title
An information-theoretic feature selection method based on estimation of Markov blanket
Abstract
Feature selection is an essential process in computational intelligence and statistical learning. It is often used to reduce the requirement of data measurement and storage and defy the curse of dimensionality in order to improve prediction performance. Although there exist many related works, it remains a challenging problem. In this paper, we first examine a set of desirable characteristics for a good feature selection method and find that most of the existing feature selection methods have fulfilled only part (not all) of these characteristics. We then propose a new feature selection method based on estimation of Markov blanket (FS-EMB) which has all the desirable characteristics. Experimental results based on benchmark data sets show that when combined with different classifiers, FS-EMB performs similar to or better than other state-of-the-art feature selection methods. More over, the performance is stable with a smaller standard deviation with respect to the average performance improvement.
Year
DOI
Venue
2015
10.1109/ICCI-CC.2015.7259406
2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
Keywords
Field
DocType
information-theoretic feature selection method,computational intelligence,statistical learning,prediction performance,estimation of Markov blanket,FS-EMB,benchmark data sets
Data mining,Data set,Dimensionality reduction,Feature selection,Computational intelligence,Feature (computer vision),Computer science,Curse of dimensionality,Artificial intelligence,Markov blanket,Machine learning,Performance improvement
Conference
Citations 
PageRank 
References 
2
0.40
11
Authors
4
Name
Order
Citations
PageRank
Hongzhi Liu18814.92
Zhonghai Wu23412.36
Xing Zhang351.79
D. Frank Hsu472266.32