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".