… and negative features for imbalanced text data. Some researchers employed IG as a
preprocessing component to rank features, which is then followed by a feature reduction technique, …

… In this research, a text classification feature selection algorithm was proposed. This
algorithm is mainly concerned with the removal of the redundant words and at the same time …

… of text classification. In this paper, we adopt novel dimension reduction methods to reduce
the … We also introduce decision functions for the centroid-based classification algorithm and …

… feature selection for text classification both theoretically and empirically. Our main result is an
unsupervised feature … power of the resultant classification function f with respect to the classi…

… In text classification, the dimensionality of the feature vector is usually huge because the …
for feature reduction are feature selection [3-10] and feature extraction [11, 12]. Feature …

… and selection techniques were applied to reduce the high dimension feature space, which
… We combined features reduction methods with PCA to reduce high dimensions features …

… is limited research about feature reduction using … reduce the feature size while increasing
the classification accuracy. We achieved feature reduction by loosely clustering similar features …

… find and predict information from text. In this study, a text classification model is built for
detecting violence in Arabic dialects on Twitter using different feature-reduction approaches. The …

… Few researchers have analysed the influence of preprocessing tasks on text classification …
reduce the dimensionality of feature space and promote the efficiency of the text classification …

… performance gained using the new method proposed in this paper doing feature reduction
is very effective and efficient especially when compared with the one gained using the …