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Bag-of-Words is the most used representation in text categorization, however it has some problems because its representation produces sparse high-dimensional feature vectors and have high feature-to-instance ratio. Feature selection is the most common approach to alleviate these problems. However, feature selection does not solve all the problems and information is lost in the process. In this paper,...
In this paper, we propose a filtering method for feature selection called ALOFT (At Least One FeaTure). The proposed method focuses on specific characteristics of text categorization domain. Also, it ensures that every document in the training set is represented by at least one feature and the number of selected features is determined in a data-driven way. We compare the effectiveness of the proposed...
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