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Abstract Feature Extraction For Text Classification, Göksel Bi̇ri̇ci̇k, Banu Di̇ri̇, Ahmet Coşkun Sönmez
Abstract Feature Extraction For Text Classification, Göksel Bi̇ri̇ci̇k, Banu Di̇ri̇, Ahmet Coşkun Sönmez
Turkish Journal of Electrical Engineering and Computer Sciences
Feature selection and extraction are frequently used solutions to overcome the curse of dimensionality in text classification problems. We introduce an extraction method that summarizes the features of the document samples, where the new features aggregate information about how much evidence there is in a document, for each class. We project the high dimensional features of documents onto a new feature space having dimensions equal to the number of classes in order to form the abstract features. We test our method on 7 different text classification algorithms, with different classifier design approaches. We examine performances of the classifiers applied on …
Hybrid Feature Selection For Text Classification, Serkan Günal
Hybrid Feature Selection For Text Classification, Serkan Günal
Turkish Journal of Electrical Engineering and Computer Sciences
Feature selection is vital in the field of pattern classification due to accuracy and processing time considerations. The selection of proper features is of greater importance when the initial feature set is considerably large. Text classification is a typical example of this situation, where the size of the initial feature set may reach to hundreds or even thousands. There are numerous research studies in the literature offering different feature selection strategies for text classification, mostly focused on filters. In spite of the extensive number of these studies, there is no significant work investigating the efficacy of a combination of features, …