Title :
Dimension reduction using evolutionary Support Vector Machines
Author :
Ang, J.H. ; Teoh, E.J. ; Tan, C.H. ; Goh, K.C. ; Tan, K.C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
Abstract :
This paper presents a novel approach of hybridizing two conventional machine learning algorithms for dimension reduction. Genetic algorithm (GA) and support vector machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population.
Keywords :
genetic algorithms; learning (artificial intelligence); support vector machines; correlation measure; dimension reduction; evolutionary support vector machines; genetic algorithm; machine learning algorithms; Bayesian methods; Biological cells; Data mining; Evolutionary computation; Genetic algorithms; Machine learning; Machine learning algorithms; Neural networks; Support vector machine classification; Support vector machines;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631290