A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data

We propose a Genetic Algorithm (GA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The GA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. Using archive records of "good" gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.

• Type: Conference
• Year: 2006
• Where: EVOW
• Authors: Edmundo Bonilla Huerta, Béatrice Duval, Jin-Kao Hao
• Keywords: Artificial Intelligence, EVOW 2006, Gene Subsets, Support Vector Machines, SVM Classifier