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

Wenjun Wang, Wei Pang, Paul A. Bingham, Mania Mania, Tzu Yu Chen, Justin J. Perry

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Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781728169293
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

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