Fast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets

Abstract

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wrapper feature selection that takes advantage of a modified method of sequential forward floating search (SFFS) algorithm. The filtering approach evaluates the features for predicting the output and complementing the other features. The candidate subset generated by the filtering approach is used by k-fold cross validation of support vector machine (SVM) with user-defined classification margin as a wrapper. Applications of the proposed SFFS method to five biomedical datasets illustrate its superiority in terms of classification accuracy and execution time relative to the conventional SFFS method and another previously improved SFFS method.