دانلود مقاله ISI انگلیسی شماره 44232
عنوان فارسی مقاله

بهینه سازی درخت باینری با استفاده از الگوریتم ژنتیک برای ماشین بردار پشتیبان چندکلاسه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
44232 2015 9 صفحه PDF سفارش دهید 7200 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Binary tree optimization using genetic algorithm for multiclass support vector machine
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 8, 15 May 2015, Pages 3843–3851

کلمات کلیدی
ماشین بردار پشتیبان - معماری درخت باینری - الگوریتم ژنتیک - متقاطع نیمه نقشه برداری
پیش نمایش مقاله
پیش نمایش مقاله بهینه سازی درخت باینری با استفاده از الگوریتم ژنتیک برای ماشین بردار پشتیبان چندکلاسه

چکیده انگلیسی

Support vector machine (SVM) with a binary tree architecture is popular since it requires the minimum number of binary SVM to be trained and tested. Many efforts have been made to design the optimal binary tree architecture. However, these methods usually construct a binary tree by a greedy search. They sequentially decompose classes into two groups so that they consider only local optimum at each node. Although genetic algorithm (GA) has been recently introduced in multiclass SVM for the local partitioning of the binary tree structure, the global optimization of a binary tree structure has not been tried yet. In this paper, we propose a global optimization method of a binary tree structure using GA to improve the classification accuracy of multiclass problem for SVM. Unlike previous researches on multiclass SVM using binary tree structures, our approach globally finds the optimal binary tree structure. For the efficient utilization of GA, we propose an enhanced crossover strategy to include the determination method of crossover points and the generation method of offsprings to preserve the maximum information of a parent tree structure. Experimental results showed that the proposed method provided higher accuracy than any other competing methods in 11 out of 18 datasets used as benchmark, within an appropriate time. The performance of our method for small size problems is comparable with other competing methods while more sensible improvements of the classification accuracy are obtained for the medium and large size problems.

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