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".
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Khani, M., Naji, H.R., Malakooti, M. (2012). A New Method for Conceptual Classification of Multi-label Texts in Web Mining Based on Ontology. In: Das, V.V., Ariwa, E., Rahayu, S.B. (eds) Signal Processing and Information Technology. SPIT 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32573-1_7
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DOI: https://doi.org/10.1007/978-3-642-32573-1_7
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