Features Selection Based ABC-SVM and PSO-SVM in Classification Problem
Mochamad Wahyudi1, Sfenrianto Sfenrianto2, Weiskhy Steven Dharmawan3

1Mochamad Wahyudi, Master of Computer Science, Postgraduate Programs STMIK Nusa Mandiri, Indonesia.
2Sfenrianto Sfenrianto, Information Systems Management Department, BINUS Graduate Program, Master of Information Systems Management, Bina Nusantara University, Jakarta.
3Weiskhy Steven Dharmawan, Master of Computer Science, Postgraduate Programs STMIK Nusa Mandiri, Indonesia.

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3855-3859 | Volume-8 Issue-12, October 2019. | Retrieval Number: L3361081219/2019©BEIESP | DOI: 10.35940/ijitee.L3361.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Feature selection can be used to improve the performance of classification algorithms. This study aims to use algorithms for feature selection in classification problems. The method in feature selection use to the Artificial Bee Colony (ABC) algorithm and Particle Swarm Optimization (PSO) based on Support Vector Machine (SVM). The ABC-SVM algorithm functions as a method of feature selection to choose the optimal subset according to the objectives set to provide the results of the classification. Then, the PSO-SVM as a comparison method in other feature selection. The results of the classification conducted by PSO-SVM with the vowel dataset is good classification (AUC 0.873), when compared with the ABC-SVM with the classification result is superior (AUC 0.996). The results of the Precision Recall and F-Measure calculations on the PSO-VSM algorithm have good classification results for sonar and wavefrom data sets. Meanwhile, the results of tests conducted by ABC-SVM get superior value from the classifier quality efficiency in the vowel data set.
Keywords:  Feature Selection, Attribute Selection, Artificial Bee Colony, Support Vector Machine
Scope of the Article:  Classification