A Comparative Study with Different Feature Selection For Arabic Text Categorization
نویسنده
چکیده
Feature Selection benefits a learner by eliminating non-informative or noisy features and by reducing the overall feature space to a manageable size. The Term Feature Selection is used in Machine Learning for the process of selecting a subset of features used to represent the text. In this paper, we propose a new approach for Text Representation based on incorporating background Knowledge Arabic WordNet with using the Feature Selection Techniques. Five methods were evaluated; including term selection based on document frequency (DF), Information Gain (IG), Mutual Information (MI), Chi-Square (CHI) and CHIR. To evaluate its accuracy, the proposed system has been trained and tested with CCA Corpus. We found Chi-Square and CHIR Statistics most effective in our experiments. Keywords—Feature selection; Machine Learning; Text Representation.
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