Evolving Committees of Support Vector Machines

Authors:
D. Valincius;A. Verikas;M. Bacauskiene;A. Gelzinis
Affiliations:
Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania and UAB "Elinta", Pramones pr. 16E, LT-51187 Kaunas,;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania and Intelligent Systems Laboratory, Halmstad University, Box 823, S-30118 Halmstad, Swe ...;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

The main emphasis of the technique developed in this work for evolving committees of support vector machines (SVM) is on a two phase procedure to select salient features. In the first phase, clearly redundant features are eliminated based on the paired t-test comparing the SVM output sensitivity-based saliency of the candidate and the noise feature. In the second phase, the genetic search integrating the steps of training, aggregation of committee members, and hyper-parameter as well as feature selection into the same learning process is employed. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real world problems have shown that significant improvements in correct classification rate can be obtained in a small number of iterations if compared to the case of using all the features available.