The aim of this study is to provide an overview the state-of-the-art elements of text
classification. For this purpose, we first select and investigate the primary and recent studies …

In the field of modern information technology, how to find information quickly, accurately and
comprehensively that users really needed has become the focus of research in this field. In …

Several machine learning and knowledge discovery approaches have been proposed for
count data modeling and classification. In particular, latent Dirichlet allocation (LDA)(Blei et …

Text categorization (TC) becomes the key technology to find relevant and timely information
from a volume of digital documents, and feature selection techniques are proposed to …

The presence of complex distributions of samples concealed in high-dimensional, massive
sample-size data challenges all of the current classification methods for data mining …

Kernel-based methods have been widely investigated in the soft-computing community.
However, they focus mainly on numeric data. In this paper, we propose a novel method for …

Symbolic data are usually composed of some categorical variables used to represent
discrete entities in many real-world applications. Mining of symbolic data is more difficult …

There are two main factors involved in documents classification, document representation
method and classification algorithm. In this study, we focus on document representation …

Today in every corporate, banking, judicial, or medical ecosystem varieties of text are
generated like customer reviews, product manuals, white papers, system logs, and usage …

The similarity based decision rule computes the similarity between a new test document and
the existing documents of the training set that belong to various categories. The new …