Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels

Wang, Wenjun, Pang, Wei, Bingham, Paul A., Mania, Mania, Chen, Tzu-Yu and Perry, Justin (2020) Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels. In: 2020 IEEE Congress on Evolutionary Computation (CEC). 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings . IEEE, Piscataway, p. 9185574. ISBN 9781728169293

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Official URL: https://doi.org/10.1109/CEC48606.2020.9185574


This paper addresses two practical problems: the classification and prediction of properties for polymer and glass materials, as a case study of evolutionary learning for tackling soft margin problems. The presented classifier is modelled by support vectors as well as various kernel functions, with its hard restrictions relaxed by slack variables to be soft restrictions in order to achieve higher performance. We have compared evolutionary learning with traditional gradient methods on standard, dual and soft margin support vector machines, built by polynomial, Gaussian, and ANOVA kernels. Experimental results for data on 434 polymers and 1,441 glasses show that both gradient and evolutionary learning approaches have their advantages. We show that within this domain the chosen gradient methodology is beneficial for standard linear classification problems, whilst the evolutionary methodology is more effective in addressing highly non-linear and complex problems, such as the soft margin problem.

Item Type: Book Section
Uncontrolled Keywords: evolutionary learning, soft margin, support vector, kernel function, slack variables
Subjects: F200 Materials Science
G100 Mathematics
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: John Coen
Date Deposited: 05 Feb 2021 11:33
Last Modified: 16 Dec 2022 16:01
URI: https://nrl.northumbria.ac.uk/id/eprint/45378

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