Feature Selection Method Using Similarity Measures and Fuzzy Entropy

This paper discusses a feature selection method utilizing similarity measures and fuzzy entropy measures, building upon P. Luukka's 2011 publication "Feature Selection Using Fuzzy Entropy Measures with Similarity Classifier." The technique is implemented through a similarity classifier that calculates fuzzy entropy to evaluate feature relevance, where lower entropy values indicate higher feature importance. This approach proves particularly valuable in various application domains including medical diagnosis, facial recognition, and speech recognition systems. By implementing this method, developers can achieve more accurate classification and prediction results with enhanced precision. The algorithm typically involves computing similarity matrices between feature vectors and applying fuzzy entropy measures to rank features according to their discriminatory power. The paper provides detailed explanations of the methodology and thorough analysis of experimental results, enabling deeper understanding of the implementation process. Key implementation steps include normalization of feature values, construction of similarity relations, and entropy-based feature ranking algorithms that can be coded using matrix operations and fuzzy set functions in programming languages like Python or MATLAB.