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

WANG, Wenjun, PANG, Wei, BINGHAM, Paul, 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). IEEE.

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Link to published version:: 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
Additional Information: © 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Glasgow, UK, 19-24 July 2020
Identification Number: https://doi.org/10.1109/CEC48606.2020.9185574
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 15 Jul 2020 13:49
Last Modified: 17 Mar 2021 22:30
URI: https://shura.shu.ac.uk/id/eprint/26632

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