Learning Semi-supervised SVM with Genetic Algorithm

Support vector machine (SVM) is an interesting classifier that has an excellent power of generalization. In this paper, we consider SVM in semi-supervised learning. We propose to use an additional criterion with the standard formulation of the transductive SVM for reinforcing the classifier regularization. Also, we use a genetic algorithm for optimizing the objective function, since the transductive SVM yields a non-convex problem. We tested our algorithm on artificial and real data, which gives promising results in comparison with Joachims' algorithm known as SVMlight TSVM.

[1]  Tommi S. Jaakkola,et al.  Partially labeled classification with Markov random walks , 2001, NIPS.

[2]  M. Seeger.  Learning with labeled and unlabeled dataMatthias , 2001 .

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Xiaojin Zhu,et al.  Semi-Supervised Learning Literature Survey , 2005 .

[8]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[11]  Tom Michael Mitchell,et al.  The Role of Unlabeled Data in Supervised Learning , 2004 .

[12]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[13]  Ayhan Demiriz,et al.  Semi-Supervised Support Vector Machines , 1998, NIPS.

[14]  Mohamed Cheriet,et al.  Optimizing resources in model selection for support vector machines , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[15]  Piero P. Bonissone,et al.  Evolutionary algorithms + domain knowledge = real-world evolutionary computation , 2006, IEEE Transactions on Evolutionary Computation.

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  Alexander Zien,et al.  Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.

[18]  Mathias M. Adankon,et al.  Optimizing resources in model selection for support vector machines , 2005 .

[19]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.