Application of feature subset selection methods on classifiers comprehensibility for bio-medical datasets

Feature subset selection is an important data reduction technique. Effects of feature selection on classifier’s accuracy are extensively studied yet comprehensibility of the resultant model is given less attention. We show that a weak feature selection method may significantly increase the complexity of a classification model. We also proposed an extendable feature selection methodology based on our preliminary results. Insights from the study can be used for developing clinical decision support systems.