Combining Probabilistic Classifiers for Text Classification

Probabilistic classifiers are considered to be among the most popular classifiers for the machine learning community and are used in many applications. Although popular probabilistic classifiers exhibit very good performance when used individually in a specific classification task, very little work has been done on assessing the performance of two or more classifiers used in combination in the same classification task. In this work, we classify documents using two probabilistic approaches: The naive Bayes classifier and the Maximum Entropy classification model. Then, we combine the results of the two classifiers to improve the classification performance, using two merging operators, Max and Harmonic Mean. The proposed method was evaluated using the “ModApte” split of the Reuters-21578 dataset and the evaluation results show a measurable improvement in the final evaluation accuracy.