Northampton Electronic Collection of Theses and Research

An Evolutionary Pentagon Support Vector Finder Method

Mousavi, S. M. H., Charles, V. and Gherman, T. (2020) An Evolutionary Pentagon Support Vector Finder Method. Expert Systems with Applications. 150, pp. 1-14. 0957-4174.

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Mousavi_Seyed_etal_Elsevier_2020_An_Evolutionary_Pentagon_Support_Vector_Finder_Method
Mousavi_Seyed_etal_Elsevier_2020_An_Evolutionary_Pentagon_Support_Vector_Finder_Method.pdf - Accepted Version
Information
Abstract:
In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results.
Uncontrolled Keywords:
Artificial Bee Colony (ABC), Big data, Data mining, Evolutionary clustering, Fuzzy C means (FCM), Pentagon Support Vector finder (PSV), Support vector
Creators:
Mousavi, S. M. H., Charles, V. and Gherman, T.
Faculties, Divisions and Institutes:
Faculties > Faculty of Business & Law > International Strategy & Business
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Date:
15 July 2020
Date Type:
Publication
Page Range:
pp. 1-14
Journal or Publication Title:
Expert Systems with Applications
Volume:
150
Number of Pages:
2524971
Language:
English
ISSN:
0957-4174
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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