Enhancing feature selection with feature maximization metric
نویسنده
چکیده
This paper deals with a new feature selection and feature contrasting approach for classification of highly unbalanced textual data with a high degree of similarity between associated classes. The efficiency of the approach is illustrated by its capacity to enhance the classification of bibliographic references into a patent classification scheme. A complementary experiment is performed on a non textual dataset issued form the UCI repository.
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تاریخ انتشار 2013