چکیده مقاله
ABSTRACT Support Vector Machines SVM have proved to be one of the most popular techniques for pattern classification that has been widely employed in many real world application areas The classification performance of the SVM largely depends on the kernel parameters setting and carefully tuning of the kernel parameters of SVM predominantly helps in the improvement in the classification performance In literature, it is evident that tuning the kernel parameters has been formulated as optimization problems and evolutionary algorithms EAs are being adopted to optimize the kernel parameters However, recently some works have concentrated on combining the SVM with meta classifier where the kernel parameters are optimized as a single objective using Differential Evolution Based on this motivation, In this paper, we would like analyze the performance of the SVM with Meta classifier by optimizing the kernel parameters through a Multi objective approach For the multi objective approach, accuracy, sensitivity and specificity are employed as the objective functions In this work, We have analyzed the performance of different Multi objective Evolutionary algorithms MOEAs such as NSGAII, NSGAIII, ISDE , PDMOEA MR and SRA for the optimization of the kernel parameters on real life dataset
کلیدواژهها
نویسندگان
Samira Ghorbanpour
School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
Vikas Palakonda
School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
Rammohan Mallipeddi
School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
شیوه ارجاع
Ghorbanpour, Samira and Palakonda, Vikas and Mallipeddi, Rammohan,1398,Multi-Objective Optimization of SVM Parameters for Meta Classifier Design,3rd International Conference on Soft Computing,Rudsar
ارائهشده در
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