A hybrid relevance measure for feature selection and its application to underwater objects recognition

This paper presents a new filter method for feature selection, which maximizes a hybrid relevance measure using a sequential forward searching scheme (mHRM-SFS). An individual relevance measure provides the classification information only in a certain aspect. Thus, we choose a modified Relief weight, mutual information, and the information entropy to formalize a hybrid relevance measure (HRM) to identify the most characterizing features. Because of its efficiency, a sequential forward searching scheme is used for maximizing the HRM. The resulting feature selection obtained by mHRM-SFS is appropriate to serve as an input for various classifiers. The mHRM-SFS can also determine the cardinality of the feature selection automatically while choosing the optimal features. Finally, the mHRM-SFS is applied to select features of underwater objects for the classification purpose. The selected features are tested by different classifiers. The classification results are compared to those of 4 existing feature selection methods.