کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970086 | 1450026 | 2017 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A BA-based algorithm for parameter optimization of Support Vector Machine
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 93, 1 July 2017, Pages 13-22
Journal: Pattern Recognition Letters - Volume 93, 1 July 2017, Pages 13-22
نویسندگان
Alaa Tharwat, Aboul Ella Hassanien, Basem E. Elnaghi,