This paper proposes an improved concept vector space (ICVS) model which takes into
account the importance of ontology concepts. Concept importance shows how important a …

This paper proposes an adaptive document representation (concept vector space model)
using Markov Chain model. The vector space representation is one of the most common …

Ontology-based document classification relies on background knowledge exploited by
ontologies to represent documents. Background knowledge is embedded in a document …

This paper provides a comprehensive performance analysis of parametric and non-
parametric machine learning classifiers including a deep feed-forward multi-layer …

Most of text categorization techniques are based on word and/or phrase analysis of the text.
Statistical analysis of a term frequency captures the importance of the term within a …

Voluminous, conveniently accessible textual documents, created and disseminated by
modern information technology, makes automated document organization increasingly …

The text document classification employs either text based approach or semantic based
approach to index and retrieve text documents. The former uses keywords and therefore …

Huge volumes of worldwide accessible information have led to the tool necessity for better
handling of massive information to overcome the conventional manual method. Thus …

An Automatic Document Classifier System based on Ontology and the Naíve Bayes
Classifier is proposed in this paper. The main concept is to first establish a keyword …

With the quick increase of information and knowledge, automatically classifying text
documents is becoming a hotspot of knowledge management. A critical capability of …