Enhancing feature selection with feature maximization metric

This paper deals with a new feature selection and f eature contrasting approach for classification of highly unbalanced textual data wi th a high degree of similarity between associated classes. The efficiency of the app roach is illustrated by its capacity to enhance the classification of bibliographic referen ces into a patent classification scheme. A complementary experiment is performed on a non textual dataset issued form the UCI repository.