Research on N-grams feature selection methods for text classification
Tsvetanka Georgieva‐Trifonova, Mahmut Duraku
Abstract
Abstract Text classification requires previously extraction of features describing the text documents in the collection. Usually these features are based on the occurrence frequency of words, N-grams of words in documents, i.e. the vector space model for document representation is built. Feature selection allows to reduce redundancy in high-dimensional representation of text data, which can significantly improve text classification performance. In the present paper, research on feature selection methods is performed in terms of the accuracy and F-measure of text classification with different number of selected attributes (N-grams of words) for different classifiers and different datasets. The obtained results can be used to apply further pre-processing steps, which include modifying the vector space model in order to achieve its improvement in terms of the text classification.