Several comparatives studies report new findings relevant to the Text Categorization (TC)
task, and all provide valuable observations. However, many of them addressed western …

A major difficulty of text categorization problems is the high dimensionality of the feature
space. Thus, feature selection is often performed in order to increase both the efficiency and …

We propose a set of (machine learning) ML-based scoring measures for conducting feature
selection. We've tested these measures on documents from two well-known corpora …

As the amount of online text increases, the demand for text categorization to aid the analysis
and management of text is increasing. Text is cheap, but information, in the form of knowing …

The problem of classification has been widely studied in the data mining, machine learning,
database, and information retrieval communities with applications in a number of diverse …

Text categorization (TC) is the task of automatically organizing a set of documents into a set
of pre-defined categories. Over the last few years, increased attention has been paid to the …

Feature selection is an important research issue in text categorization. The reason for this is
that thousands of features are often involved, even when the simplest document …

This paper investigates the effect of a cascaded feature selection (CFS) in SVMs text
categorization. Unlike existing feature selections, our method (CFS) has two advantages …

With the proliferation of unstructured data, text classification or text categorization has found
many applications in topic classification, sentiment analysis, authorship identification, spam …

Text categorization is widely used when organizing documents in a digital form. Due to the
increasing number of documents in digital form, automated text categorization has become …