Multiple Explanations Driven Naïve Bayes Classifier

Ahmad Almonayyes (Kuwait University, Kuwait)

Abstract: Exploratory data analysis over foreign language text presents virtually untapped opportunity. This work incorporates Naïve Bayes classifier with Case-Based Reasoning in order to classify and analyze Arabic texts related to fanaticism. The Arabic vocabularies are converted to equivalent English words using conceptual hierarchy structure. The understanding process operates at two phases. At the first phase, a discrimination network of multiple questions is used to retrieve explanatory knowledge structures each of which gives an interpretation of a text according to a particular aspect of fanaticism. Explanation structures organize past documents of fanatic content. Similar documents are retrieved to generate additional valuable information about the new document. In the second phase, the document classification process based on Naïve Bayes is used to classify documents into their fanatic class. The results show that the classification accuracy is improved by incorporating the explanation patterns with the Naïve Bayes classifier.

Keywords: Naïve Bayes, case-based reasoning, data mining, explanation patterns, text classification

Categories: I.1.2, I.1.7, I.2.1, I.2.6