scholarly journals Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 116875-116885 ◽  
Author(s):  
G. S. Thejas ◽  
Sajal Raj Joshi ◽  
S. S. Iyengar ◽  
N. R. Sunitha ◽  
Prajwal Badrinath
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Feature Selection Method ◽  
Selection Method ◽  
Normalized Mutual Information

A novel feature selection method based on normalized mutual information

2011 ◽  
Vol 37 (1) ◽  
pp. 100-120 ◽  
Author(s):  
La The Vinh ◽  
Sungyoung Lee ◽  
Young-Tack Park ◽  
Brian J. d’Auriol
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Feature Selection Method ◽  
Selection Method ◽  
Normalized Mutual Information

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.

scholarly journals A redundancy-removing feature selection algorithm for nominal data

2015 ◽  
Vol 1 ◽  
pp. e24 ◽  
Author(s):  
Zhihua Li ◽  
Wenqu Gu
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Feature Selection Method ◽  
Selection Method ◽  
Selection Algorithm ◽  
Nominal Data ◽  
New Information ◽  
New Feature ◽  
High Dimensional Datasets ◽  
Experimental Comparisons

No order correlation or similarity metric exists in nominal data, and there will always be more redundancy in a nominal dataset, which means that an efficient mutual information-based nominal-data feature selection method is relatively difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.

scholarly journals Input Feature Selection Method Based on Feature Set Equivalence and Mutual Information Gain Maximization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 151525-151538 ◽  
Author(s):  
Xinzheng Wang ◽  
Bing Guo ◽  
Yan Shen ◽  
Chimin Zhou ◽  
Xuliang Duan
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Information Gain ◽  
Feature Selection Method ◽  
Selection Method ◽  
Input Feature
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