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Neural Computing and Applications

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13-03-2017 | Original Article

A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm

Authors: Omid Naghash Almasi, Mohammad Hassan Khooban

Published in: Neural Computing and Applications | Issue 11/2018

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Abstract

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.

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Title: A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm
Authors: Omid Naghash Almasi
Mohammad Hassan Khooban
Publication date: 13-03-2017
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2930-y

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