Automated text classification has been considered as a vital method to manage and process
a vast amount of documents in digital forms that are widespread and continuously …

Feature selection and instance selection are two important data preprocessing steps in data
mining, where the former is aimed at removing some irrelevant and/or redundant features …

Many real-world text classification tasks involve imbalanced training examples. The
strategies proposed to address the imbalanced classification (eg, resampling, instance …

Data reduction, aiming to reduce the original data by selecting the most representative
information, is an important technique of preprocessing data. At present, large-scale or huge …

We consider feature selection for text classification both theoretically and empirically. Our
main result is an unsupervised feature selection strategy for which we give worst-case …

A method for adapting to a shift in document content according to one embodiment of the
present invention includes receiving at least one labeled seed document; receiving …

Methods for classifying documents are presented. Methods for analyzing documents
associated with legal discovery are also presented. Methods for cleaning up data are also …

Due to technological advancement, data size has seen a significant increase both in terms
of features and instances. An efficient way to handle large sized datasets is to apply data …

In this informative age, we find many documents are available in digital forms which need
classification of the text. For solving this major problem present researchers focused on …

This paper introduces a novel enhancement for unsupervised feature selection based on
generalized Dirichlet (GD) mixture models. Our proposal is based on the extension of the …