Combining Naive Bayes and Modified Maximum Entropy Classifiers for Text Classification
Text Classificat ion is done mainly through classifiers proposed over the years, Naive Bayes and Maximum 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 Maximum Entropy based classifier. Maximum 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 Naive Bayes classifier. Naive Bayes Classification is a very simple and fast technique. The assumption model is opposite to that of Maximum 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 modifications of Maximum Entropy, 3 combined classifiers) are demonstrated through implementation and experimenting on real life datasets.