Loading...
An evolutionary Pentagon Support Vector finder method
Mousavi, S.M.H. Vincent, Charles Gherman, T.
Mousavi, S.M.H.
Vincent, Charles
Gherman, T.
Publication Date
15/07/2020
End of Embargo
Supervisor
Rights
© 2020 Elsevier Ltd. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
05/02/2020
Institution
Department
Awarded
Embargo end date
Collections
Additional title
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.
Version
Accepted manuscript
Citation
Mousavi SMH, Vincent C and Gherman T (2020) An evolutionary Pentagon Support Vector finder method. Expert Systems with Applications. 150: 113284.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Article