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, Naive 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".
['Mahnaz Khani', 'Hamid Reza Naji', 'Mohammad V. Malakooti']
A New Method for Conceptual Classification of Multi-label Texts in Web Mining Based on Ontology