  In this paper, based on a fuzzy entropy feature selection framework,
different methods have been implemented and compared to improve the key
components of the framework. Those methods include the combinations of three
ideal vector calculations, three maximal similarity classifiers and three fuzzy
entropy functions. Different feature removal orders based on the fuzzy entropy
values were also compared. The proposed method was evaluated on three publicly
available biomedical datasets. From the experiments, we concluded the optimized
combination of the ideal vector, similarity classifier and fuzzy entropy
function for feature selection. The optimized framework was also compared with
other six classical filter-based feature selection methods. The proposed method
was ranked as one of the top performers together with the Correlation and
ReliefF methods. More importantly, the proposed method achieved the most stable
performance for all three datasets when the features being gradually removed.
This indicates a better feature ranking performance than the other compared
methods.
