Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Framework
This work provides an incremental optimization of a feature selection framework for biomedical data analysis.
The authors optimized a fuzzy entropy-based feature selection and classification framework by testing combinations of ideal vector calculations, similarity classifiers, and fuzzy entropy functions, along with different feature removal orders. On three biomedical datasets, the optimized framework achieved top performance comparable to Correlation and ReliefF methods, with the most stable performance as features were gradually removed.
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.