Feature selection using multivariate adaptive regression splines in telecommunication fraud detection
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 t...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/93090/1/MuhalimMohamedAmin2020_FeatureSelectionUsingMultivariateAdaptiveRegression.pdf |
| _version_ | 1796865553215782912 |
|---|---|
| author | Mohamed Amin, M. Zainal, A. Mohd. Azmi, N. F. Ali, N. A. |
| author_facet | Mohamed Amin, M. Zainal, A. Mohd. Azmi, N. F. Ali, N. A. |
| author_sort | Mohamed Amin, M. |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T20:58:45Z |
| format | Conference or Workshop Item |
| id | utm.eprints-93090 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:58:45Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | utm.eprints-930902021-11-07T05:54:48Z http://eprints.utm.my/93090/ Feature selection using multivariate adaptive regression splines in telecommunication fraud detection Mohamed Amin, M. Zainal, A. Mohd. Azmi, N. F. Ali, N. A. QA75 Electronic computers. Computer science 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. 2020 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/93090/1/MuhalimMohamedAmin2020_FeatureSelectionUsingMultivariateAdaptiveRegression.pdf Mohamed Amin, M. and Zainal, A. and Mohd. Azmi, N. F. and Ali, N. A. (2020) Feature selection using multivariate adaptive regression splines in telecommunication fraud detection. In: 2nd Joint Conference on Green Engineering Technology and Applied Computing 2020, IConGETech 2020 and International Conference on Applied Computing 2020, ICAC 2020, 4-5 Feb 2020, Bangkok, Thailand. http://dx.doi.org/10.1088/1757-899X/864/1/012059 |
| spellingShingle | QA75 Electronic computers. Computer science Mohamed Amin, M. Zainal, A. Mohd. Azmi, N. F. Ali, N. A. Feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| title | Feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| title_full | Feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| title_fullStr | Feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| title_full_unstemmed | Feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| title_short | Feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| title_sort | feature selection using multivariate adaptive regression splines in telecommunication fraud detection |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/93090/1/MuhalimMohamedAmin2020_FeatureSelectionUsingMultivariateAdaptiveRegression.pdf |
| work_keys_str_mv | AT mohamedaminm featureselectionusingmultivariateadaptiveregressionsplinesintelecommunicationfrauddetection AT zainala featureselectionusingmultivariateadaptiveregressionsplinesintelecommunicationfrauddetection AT mohdazminf featureselectionusingmultivariateadaptiveregressionsplinesintelecommunicationfrauddetection AT alina featureselectionusingmultivariateadaptiveregressionsplinesintelecommunicationfrauddetection |