Abstract-TextClassificat ion is done mainly through classifiers proposed over the years, Naï ve Bayes and Maximu m Entropy being the most popular of all. However, the individual classifiers show limited applicability according to their respective domains and scopes. Recent research works evaluated that the combination of classifiers when used for classification showed better performance than the individual ones. This work introduces a modified Maximu m Entropy-based classifier. Maximu m Entropy classifiers provide a great deal of flexibility for parameter defin itions and follow assumptions closer to real world scenario. This classifier is then combined with a Naï ve Bayes classifier. Naï ve Bayes Classification is a very simp le and fast technique. The assumption model is opposite to that of Maximu m Entropy. The combination of classifiers is done through operators that linearly co mbine the results of two classifiers to predict class of documents in query. Proper validation of the 7 proposed modifications (4 mod ifications of Maximu m Entropy, 3 combined classifiers) are demonstrated through imp lementation and experimenting on real life datasets.
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