Title :
Using evolutionary computation to improve SVM classification
Author :
Kamath, Uday ; Shehu, Amarda ; De Jong, Kenneth
Author_Institution :
Norkom Technol. in Reston, Reston, VA, USA
Abstract :
Support vector machines (SVMs) are now one of the most popular machine learning techniques for solving difficult classification problems. Their effectiveness depends on two critical design decisions: 1) mapping a decision problem into an n-dimensional feature space, and 2) choosing a kernel function that maps the n-dimensional feature space into a higher dimensional and more effective classification space. The choice of kernel functions is generally limited to a small set of well-studied candidates. However, the choice of a feature set is much more open-ended without much design guidance. In fact, many SVMs are designed with standard generic feature space mappings embedded a priori. In this paper we describe a procedure for using an evolutionary algorithm to design more compact non-standard feature mappings that, for a fixed kernel function, significantly improves the classification accuracy of the constructed SVM.
Keywords :
decision theory; evolutionary computation; pattern classification; support vector machines; decision problem mapping; evolutionary computation; generic feature space mapping; kernel function; machine learning technique; support vector machine classification; Accuracy; Bioinformatics; DNA; Evolutionary computation; Kernel; Machine learning; Support vector machines;
Conference_Titel :
Evolutionary Computation (CEC), 2010 IEEE Congress on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6909-3
DOI :
10.1109/CEC.2010.5586432