FW_SFS: A NOVEL FEATURE SELECTION METHOD FOR POWER QUALITY DATA MINING

2010 ◽  
Vol 30 (2) ◽  
Author(s):  
R. Ahila ◽  
M. Sudhakaran ◽  
K. Manimala
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Power Quality ◽  
Feature Selection Method ◽  
Selection Method ◽  
Quality Data

scholarly journals Feature selection based on weighted conditional mutual information

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongfang Zhou ◽  
Xiqian Wang ◽  
Yao Zhang
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Standard Deviation ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Conditional Mutual Information ◽  
The Core ◽  
Essential Step

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

A Hybrid Feature Selection Method for Effective Data Classification in Data Mining Applications

2019 ◽  
Vol 11 (1) ◽  
pp. 1-16
Author(s):  
Ilangovan Sangaiya ◽  
A. Vincent Antony Kumar
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Feature Subset ◽  
Data Set ◽  
Optimal Feature Subset ◽  
Optimal Feature

In data mining, people require feature selection to select relevant features and to remove unimportant irrelevant features from a original data set based on some evolution criteria. Filter and wrapper are the two methods used but here the authors have proposed a hybrid feature selection method to take advantage of both methods. The proposed method uses symmetrical uncertainty and genetic algorithms for selecting the optimal feature subset. This has been done so as to improve processing time by reducing the dimension of the data set without compromising the classification accuracy. This proposed hybrid algorithm is much faster and scales well to the data set in terms of selected features, classification accuracy and running time than most existing algorithms.

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