Genetic algorithm–based training for semi-supervised SVM

Authors:
Mathias M. Adankon;Mohamed Cheriet
Affiliations:
University of Quebec, Synchromedia Laboratory, École de Technologie Supérieure, 1100 Notre-Dame West, H3C 1K3, Montreal, Canada;University of Quebec, Synchromedia Laboratory, École de Technologie Supérieure, 1100 Notre-Dame West, H3C 1K3, Montreal, Canada
Venue:
Neural Computing and Applications
Year:
2010

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Abstract

The Support Vector Machine (SVM) is an interesting classifier with excellent power of generalization. In this paper, we consider applying the SVM to semi-supervised learning. We propose using an additional criterion with the standard formulation of the semi-supervised SVM (S 3 VM) to reinforce classifier regularization. Since, we deal with nonconvex and combinatorial problem, we use a genetic algorithm to optimize the objective function. Furthermore, we design the specific genetic operators and certain heuristics in order to improve the optimization task. We tested our algorithm on both artificial and real data and found that it gives promising results in comparison with classical optimization techniques proposed in literature.