A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:142-151, 2010.
This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (Improved F-score and Sequential Forward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In our IFSFFS, we firstly generalized the original F-score to the improved F-score measuring the discrimination of more than two sets of real numbers. Then we proposed to combine Sequential Forward Floating Search (SFFS) and our improved F-score to accomplish the optimal feature subset selection. Where, our improved F-score is an evaluation criterion for filters, while SFFS and SVM compose an evaluation system of wrappers. The best parameters of kernel function of SVM are found out by grid search technique with ten-fold cross validation. Experiments have been conducted on five random training-test partitions of the erythemato-squamous diseases dataset from UCI machine learning database. The experimental results show that our SVM-based model with IFSFFS achieved the optimal classification accuracy with no more than 14 features as well.