Supervised Classification and Gene Selection Using Simulated Annealing

Genomic data are often characterized by small cardinality and high dimensionality. For those data, a feature selection procedure could highlight the relevant genes and improve the classification results. In this paper we propose a wrapper approach to gene selection in classification of gene expression data using simulated annealing and SVM. The proposed approach can do global combinatorial searches through the space of possible input subsets, can handle cases with numerical, categorical or mixed inputs, and is able to find (sub-)optimal subsets of input variables giving very low classification errors. The method has been tested on the publicly available data sets Leukemia by Golub et al. and Colon by Alon at al. The experimental results highlight the capacity of the method to select minimal sets of relevant genes.