Logitboost of Multinomial Bayesian Classifier for Text Classification
نویسندگان
چکیده
Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The Multinomial Bayesian Classifier has traditionally been a focus of research in the field of text learning. This paper increases the accuracy of Multinomial Bayesian Classifier with the usage of the Logitboost technique. We performed a large-scale comparison on benchmark datasets with other state-of-the-art algorithms and the proposed technique had greater accuracy in most cases. Copyright © 2006 Praise Worthy Prize All rights reserved.
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