Why Feature Selection in Data Mining Is Prominent? A Survey

  • M. Durairaj
  • T. S. Poornappriya
Conference paper


Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today’s world situations. Current feature selection techniques primarily concentrate on obtaining relevant attributes. This paper presents the notions of feature relevance, redundancy, evaluation criteria, and literature survey on the feature selection approaches in the different areas by many researchers. This paper supports to choose feature selection techniques without identifying the knowledge of every algorithm.


Relevance Redundancy Feature selection Filter-based approach Wrapper-based approach Classification techniques 



Average Accuracy


Akaike information criterion


Artificial Neural Network


Area under the Curve


Binary Wolf Optimization


Classification and Regression Tree


Cuttlefish algorithm


Correlation-based Feature Selection




Data Mining




Fast Correlation-based Feature selection


False Positive


Genetic Algorithm


Gain Ratio


Information Gain


K-Nearest Neighbor


Logistic Model Tree


Mutual Information


Multi-Layer Perceptron


Naïve Bayes


Overall Accuracy




Principal Component Analysis


Particle Swarm Optimization




Radial Basis Function


Receiver Operating Curve


Support Vector Machine


True Positive


Term Variance


Whale Optimization algorithm


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • M. Durairaj
    • 1
  • T. S. Poornappriya
    • 1
  1. 1.School of Computer Science, Engineering and ApplicationsBharathidasan UniversityTiruchirappalliIndia

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