Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2017) Integrated ACOR/IACOMV-R-SVM Algorithm. International Scholarly and Scientific Research & Innovation, 11 (12). 1309 -1313. ISSN 0000000091950263

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Abstract

A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.

Item Type:	Article
Uncontrolled Keywords:	Continuous ant colony optimization, incremental continuous ant colony, simultaneous optimization, support vector machine.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	11 Nov 2020 06:04
Last Modified:	11 Nov 2020 06:04
URI:	https://repo.uum.edu.my/id/eprint/27874

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