Open AccessFeature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection
Author(s) -
Muhalim Mohamed Amin,
Anazida Zainal,
Nurulhuda Firdaus Mohd Azmi,
Nor Azizah Ali
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012059
Subject(s) - artificial intelligence , feature selection , multivariate adaptive regression splines , support vector machine , computer science , classifier (uml) , pattern recognition (psychology) , artificial neural network , dimensionality reduction , decision tree , curse of dimensionality , machine learning , multivariate statistics , computation , data mining , regression analysis , bayesian multivariate linear regression , algorithm
Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving the highest accuracy (97.44%) compared to other classifiers.