TY - GEN
T1 - Evolutionary Learning for Soft Margin Problems
T2 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020
AU - Wang, Wenjun
AU - Pang, Wei
AU - Bingham, Paul A.
AU - Mania, Mania
AU - Chen, Tzu Yu
AU - Perry, Justin J.
N1 - Funding Information:
This research is supported by the Engineering and Physical Sciences Research Council (EPSRC) funded project New Industrial Systems: Manufacturing Immortality (EP/R020957/1). * Corresponding author.
Funding Information:
ACKNOWLEDGMENTS This research is supported by the Engineering and Physical Sciences Research Council (EPSRC) funded Project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1). The authors are also grateful to the Manufacturing Immortality consortium.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - evolutionary learning
KW - kernel function
KW - slack variables
KW - soft margin
KW - support vector
UR - http://www.scopus.com/inward/record.url?scp=85092026923&partnerID=8YFLogxK
U2 - 10.1109/CEC48606.2020.9185574
DO - 10.1109/CEC48606.2020.9185574
M3 - Conference contribution
AN - SCOPUS:85092026923
T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
BT - 2020 IEEE Congress on Evolutionary Computation (CEC)
PB - IEEE
CY - Piscataway
Y2 - 19 July 2020 through 24 July 2020
ER -