Hybrid feature selection method for biomedical datasets

Currently classifying high-dimensional data is a very challenging problem. High dimensional feature spaces affect both accuracy and efficiency of supervised learning methods. To address this issue, we present a fast and efficient feature selection algorithm to facilitate classifying high-dimensional datasets as those appearing in Bioinformatics problems. Our method employs a Laplacian score ranking to reduce the search space, combined with a simple wrapper strategy to find a good feature subset of uncorrelated features, giving as result a hybrid feature selection method which is useful for high dimensional spaces. Some experiments have been carried out on gene microarray datasets to demonstrate the effectiveness and robustness of the proposed method.