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Why Feature Selection in Data Mining Is Prominent? A Survey

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Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications (AISGSC 2019 2019)

Abstract

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.

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Abbreviations

AA:

Average Accuracy

AIC:

Akaike information criterion

ANN:

Artificial Neural Network

AUC:

Area under the Curve

BWO:

Binary Wolf Optimization

CART:

Classification and Regression Tree

CFA:

Cuttlefish algorithm

CFS:

Correlation-based Feature Selection

CS:

Chi-Square

DM:

Data Mining

F:

F-Score

FCBF:

Fast Correlation-based Feature selection

FP:

False Positive

GA:

Genetic Algorithm

GR:

Gain Ratio

IG:

Information Gain

K-NN:

K-Nearest Neighbor

LMT:

Logistic Model Tree

MI:

Mutual Information

MLP:

Multi-Layer Perceptron

NB:

Naïve Bayes

OA:

Overall Accuracy

P:

Precision

PCA:

Principal Component Analysis

PSO:

Particle Swarm Optimization

R:

Recall

RBF:

Radial Basis Function

ROC:

Receiver Operating Curve

SVM:

Support Vector Machine

TP:

True Positive

TV:

Term Variance

WOA:

Whale Optimization algorithm

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Author information

Authors and Affiliations

  1. School of Computer Science, Engineering and Applications, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

    M. Durairaj & T. S. Poornappriya

Authors
  1. M. Durairaj
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  2. T. S. Poornappriya
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Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    L. Ashok Kumar

  2. Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    L. S. Jayashree

  3. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    R. Manimegalai

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Durairaj, M., Poornappriya, T.S. (2020). Why Feature Selection in Data Mining Is Prominent? A Survey. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_88

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  • DOI: https://doi.org/10.1007/978-3-030-24051-6_88

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24050-9

  • Online ISBN: 978-3-030-24051-6

  • eBook Packages: EngineeringEngineering (R0)

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