A New Method for Conceptual Classification of Multi-label Texts in Web Mining Based on Ontology

Mahnaz Khani; Hamid Naji; Mohammad Malakooti

Signal Processing and Information Technology. First International Joint Conference, SPIT 2011 and IPC 2011, Amsterdam, The Netherlands, December 1-2, 2011, Revised Selected Papers

Research Article

A New Method for Conceptual Classification of Multi-label Texts in Web Mining Based on Ontology

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@INPROCEEDINGS{10.1007/978-3-642-32573-1_7,
    author={Mahnaz Khani and Hamid Naji and Mohammad Malakooti},
    title={A New Method for Conceptual Classification of Multi-label Texts in Web Mining Based on Ontology},
    proceedings={Signal Processing and Information Technology. First International Joint Conference, SPIT 2011 and IPC 2011, Amsterdam, The Netherlands, December 1-2, 2011, Revised Selected Papers},
    proceedings_a={SPIT \& IPC},
    year={2012},
    month={10},
    keywords={Ontology TSR Conceptual Classification Web Mining},
    doi={10.1007/978-3-642-32573-1_7}
}

Mahnaz Khani
Hamid Naji
Mohammad Malakooti
Year: 2012
A New Method for Conceptual Classification of Multi-label Texts in Web Mining Based on Ontology
SPIT & IPC
Springer
DOI: 10.1007/978-3-642-32573-1_7

Mahnaz Khani¹^,*, Hamid Naji¹^,*, Mohammad Malakooti¹^,*

1: Islamic Azad University

*Contact email: mahnaz_khani2000@yahoo.com, hamidnaji@ieee.org, malakooti@iau.ae

Abstract

This paper presents a new inductive learning method for conceptual classification of multi-label texts in web mining based on ontology through Term Space Reduction (TSR) and through using mutual information measure. Laboratory results show the presented method has high precision in compare to existing methods of SVM, Find Similar, Naïve Bayes Nets, and Decision Trees. It should be noted that break–even point is used in micro–averaging for appropriate classification of data complex entitled "Reuters–21578 Apte Split".

Keywords: Ontology TSR Conceptual Classification Web Mining

Published: 2012-10-08

: http://dx.doi.org/10.1007/978-3-642-32573-1_7