scholarly journals Parameter Selection in Mutual Information-Based Feature Selection in Automated Diagnosis of Multiple Epilepsies Using Scalp EEG

2012 ◽  
Author(s):  
Wesley T. Kerr ◽  
Ariana Anderson ◽  
Hongjing Xia ◽  
Eric S. Braun ◽  
Edward P. Lau ◽  
...  
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Parameter Selection ◽  
Automated Diagnosis ◽  
Scalp Eeg

Hofs: Higher Order Mutual Information Approximation for Feature Selection in R

2022 ◽  
Author(s):  
Krzysztof Gajowniczek ◽  
Jialin Wu ◽  
Soumyajit Gupta ◽  
Chandrajit Bajaj
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Higher Order

Feature Selection Method Based on Mutual Information and Support Vector Machine

2021 ◽  
pp. 2150021
Author(s):  
Gang Liu ◽  
Chunlei Yang ◽  
Sen Liu ◽  
Chunbao Xiao ◽  
Bin Song
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Standard Data ◽  
Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

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