An evolutionary approach to transduction in support vector machines

An evolutionary approach to the training of transductive support vector machines (TSVMs) is presented, with a gene-dependent mutation operator, motivated by the k-nearest neighbor algorithm, accelerating the convergence significantly.

Abstract

This paper presents an evolutionary approach to the training of transductive support vector machines (TSVMs). A genetic algorithm (GA) is used to search for the best labeling of the test set, providing increased convergence performance and more globally optimized solutions. The stochastic nature of GAs makes this approach more likely to reach global minima than the standard transductive SVMs. A gene-dependent mutation operator, motivated by the k-nearest neighbor algorithm, is introduced, accelerating the convergence significantly.