Turkish Journal of Electrical Engineering and Computer Sciences, ( ISI ), Volume (24), No (1), Year (2015-12) , Pages (219-233)

Title : ( Fast and de-noise support vector machine training method based on fuzzy clustering method for large real world datasets )

Authors: امید الماسی نقاش , Modjtaba Rouhani ,

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Abstract

Classifying large and real-world datasets is a challenging problem in machine learning algorithms. Among the machine learning methods, the support vector machine (SVM) is a well-known approach with high generalization ability. Unfortunately, while the number of training data increases and the data contain noise, the performance of SVM signi cantly decreases. In this paper, a fast and de-noise two-stage method for training SVMs to deal with large, real- world datasets is proposed. In the rst stage, data that contain noises or are suspected to be noisy are identi ed and eliminated from the genuine training dataset. The process of elimination and identi cation is based on the movement of the center of the convex hull data in the training dataset. The convex hull data are computed via the QHull algorithm. On the other hand, the well-known fuzzy clustering method (FCM) is applied to compress and reduce the size of the training dataset. Finally, the reduced and puri ed cluster centers are used for training the SVM. A set of experiments is conducted on the four benchmarking datasets of the UCI database. Moreover, the amount of training time and the generalization of the proposed approach are compared with FCM-SVM and normal SVM. The results indicate that the proposed method reduces the amount of training time and has a considerable success in removing noisy data from the training dataset. Therefore, the proposed method can achieve a higher generalization performance in comparison with the other methods in large, real-world datasets.

Keywords

, Key words: Support vector machine, fuzzy clustering method, convex hull, QHull algorithm, reduction set method, noisy training dataset
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@article{paperid:1053358,
author = {امید الماسی نقاش and Rouhani, Modjtaba},
title = {Fast and de-noise support vector machine training method based on fuzzy clustering method for large real world datasets},
journal = {Turkish Journal of Electrical Engineering and Computer Sciences},
year = {2015},
volume = {24},
number = {1},
month = {December},
issn = {1300-0632},
pages = {219--233},
numpages = {14},
keywords = {Key words: Support vector machine; fuzzy clustering method; convex hull; QHull algorithm; reduction set method; noisy training dataset},
}

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%0 Journal Article
%T Fast and de-noise support vector machine training method based on fuzzy clustering method for large real world datasets
%A امید الماسی نقاش
%A Rouhani, Modjtaba
%J Turkish Journal of Electrical Engineering and Computer Sciences
%@ 1300-0632
%D 2015

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