Parameter optimization and feature selection for support vector machine by Multi-Objective IPO algorithm ( MOIPO )

سال انتشار: 1394
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 677

فایل این مقاله در 14 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

COMCONF01_368

تاریخ نمایه سازی: 8 آذر 1394

چکیده مقاله:

Support vector machine is a new classifier, based on the structured risk minimization principle. The performance of the SVM, depends on different parameters such as: penalty factor, C, and the kernel factor, σ. Also choosing an appropriate kernel function can improve the Recognition Score and lower the amount of computation. Furthermore selecting, the useful features among several features in the dataset not only increases the performance of the SVM, but also reduces the computation time and complexity. So this is an optimization problem which can be solved by a heuristic algorithm. In some cases besides the Recognition Score, the Reliability of the classifier’s output, is important. So in such cases a multi-objective optimization algorithm is needed. In this paper we have got the MOIPO3 algorithm to optimize the parameters of the SVM, choose appropriate kernel function and select the best features simultaneously in order to increase the Recognition Score and the Reliability of the SVM. Three different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOIPO-SVM). The results of the proposed method are compared to those which are achieved by RBF and MLP neural networks.

نویسندگان

Oveis Dehghantanha

Department of Electrical Engineering, MSc student, University of Birjand,

Iman Behravan

Department of Electrical Engineering, MSc student, University of Birjand

Seyed Hamid Zahiri

Department of Electrical Engineering, Faculty of Engineering, University of Birjand