This paper proposes a new approach for text categorization, based on a feature projection
technique. In our approach, training data are represented as the projections of training …

This paper proposes a new approach for text categorization, based on a feature projection
technique. In our approach, training data are represented as the projections of training …

The preferred feature selection methods for text classification are filter-based. In a common
filter-based feature selection scheme, unique scores are assigned to features; then, these …

In this paper, we propose a new feature-selection algorithm for text classification, called best
terms (BT). The complexity of BT is linear in respect to the number of the training-set …

The main objective is to propose a text classification based on the features selection and
preprocessing thereby reducing the dimensionality of the Feature vector and increase the …

Feature selection is an efficient strategy to reduce the dimensionality of data and removing
the noise in text categorization. However, most feature selection methods aim to remove non …

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 …

In news portals, text category information is needed for news presentation. However, for
many news stories the category information is unavailable, incorrectly assigned or too …

Text Categorization is the process of automatically assigning predefined categories to free
text documents. Feature weight, which calculates feature (term) values in documents, is one …

Text categorization presents unique challenges to traditional classification methods due to
the large number of features inherent in the datasets from real-world applications of text …