{"title":"Integrated ACOR\/IACOMV-R-SVM Algorithm","authors":"Hiba Basim Alwan, Ku Ruhana Ku-Mahamud","volume":132,"journal":"International Journal of Computer and Information Engineering","pagesStart":1309,"pagesEnd":1314,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10008369","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, ACO_{R<\/sub>-SVM, will tune SVM parameters, while the second IACOMV-R<\/sub>-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.","references":"[1]\tChen, W., Chun-Na, L., Hua-Xin, P., Yan-Ru, G., & Yuan-Hai, S. (2017). Alternating direction method of multipliers for L1- and L2-norm best fitting hyper plane classifier. Procedia Computer Science, 108C (2017), 1292-1301.\r\n[2]\tKojo, G, James, B., Andrew H., & Elena, G. (2017). Linear classifier design under heteroscedasticity in linear discriminant analysis. Expert System with Applications, 79 (15 Augest 2017), 4-52.\r\n[3]\tAcharya, J., Das, A., Orlitsky, A., Pan, S. & Santhanam, N. (2010). Classification using pattern probability estimators. 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