An inverse problem of support vector machines (SVMs) was investigated. 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. An immunogenetic particle swarm incorporated intelligence algorithm was proposed to solve this problem. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability. The application in the recognition of the bank risk shows it is effective.
['Jingmin Wang', 'Guoqiao Ren']
The Inverse Problem of Support Vector Machines Solved by a New Intelligence Algorithm