کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
388881 660946 2008 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Particle swarm optimization for parameter determination and feature selection of support vector machines
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Particle swarm optimization for parameter determination and feature selection of support vector machines
چکیده انگلیسی

Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without reducing SVM classification accuracy. A particle swarm optimization (PSO) based approach for parameter determination and feature selection of the SVM, termed PSO + SVM, is developed.Several public datasets are employed to calculate the classification accuracy rate in order to evaluate the developed PSO + SVM approach. The developed approach was compared with grid search, which is a conventional method of searching parameter values, and other approaches. Experimental results demonstrate that the classification accuracy rates of the developed approach surpass those of grid search and many other approaches, and that the developed PSO + SVM approach has a similar result to GA + SVM. Therefore, the PSO + SVM approach is valuable for parameter determination and feature selection in an SVM.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 35, Issue 4, November 2008, Pages 1817–1824
نویسندگان
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