Letters: A genetic algorithm for solving the inverse problem of support vector machines

Authors:
Xi-Zhao Wang;Qiang He;De-Gang Chen;Daniel Yeung
Affiliations:
Department of Mathematics and Computer Science, Hebei University, No. 88, Hezuo Road, Baoding, Hebei 071002, China;Department of Mathematics and Computer Science, Hebei University, No. 88, Hezuo Road, Baoding, Hebei 071002, China;Department of Mathematics and Physics, North China Electric Power University, Beijing, China;Department of Computing, Hong Kong Polytechnic University, Kawloon, Hong Kong
Venue:
Neurocomputing
Year:
2005

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Abstract

This paper investigates an inverse problem of support vector machines (SVMs). The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains the maximum. Here the margin is defined according to the separating hyper-plane generated by support vectors. It is difficult to give an exact solution to this problem. In this paper, we design a genetic algorithm to solve this problem. Numerical simulations show the feasibility and effectiveness of this algorithm. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability.