Research on feature selection algorithm based on mutual information and genetic algorithm

2014 ◽  
Author(s):  
Pan-Shi Tang ◽  
Xiao-Long Tang ◽  
Zhong-Yu Tao ◽  
Jian-Ping Li
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

A NOVEL FEATURE SELECTION ALGORITHM WITH SUPERVISED MUTUAL INFORMATION FOR CLASSIFICATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350027
Author(s):  
JAGANATHAN PALANICHAMY ◽  
KUPPUCHAMY RAMASAMY
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Class A ◽  
Selection Algorithms ◽  
The Relationship ◽  
Class Variable

Feature selection is essential in data mining and pattern recognition, especially for database classification. During past years, several feature selection algorithms have been proposed to measure the relevance of various features to each class. A suitable feature selection algorithm normally maximizes the relevancy and minimizes the redundancy of the selected features. The mutual information measure can successfully estimate the dependency of features on the entire sampling space, but it cannot exactly represent the redundancies among features. In this paper, a novel feature selection algorithm is proposed based on maximum relevance and minimum redundancy criterion. The mutual information is used to measure the relevancy of each feature with class variable and calculate the redundancy by utilizing the relationship between candidate features, selected features and class variables. The effectiveness is tested with ten benchmarked datasets available in UCI Machine Learning Repository. The experimental results show better performance when compared with some existing algorithms.

An Improved Feature Selection Algorithm Based on Parzen Window and Conditional Mutual Information

2013 ◽  
Vol 347-350 ◽  
pp. 2614-2619
Author(s):  
Deng Chao He ◽  
Wen Ning Hao ◽  
Gang Chen ◽  
Da Wei Jin
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Probability Density Functions ◽  
Density Functions ◽  
Continuous Variables ◽  
Conditional Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Parzen Window ◽  
Selection For

In this paper, an improved feature selection algorithm by conditional mutual information with Parzen window was proposed, which adopted conditional mutual information as an evaluation criterion of feature selection in order to overcome the deficiency of feature redundant and used Parzen window to estimate the probability density functions and calculate the conditional mutual information of continuous variables, in such a way as to achieve feature selection for continuous data.

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