An enhanced EM method of semi-supervised classification based on Naive Bayesian

• Published in: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery Vol. 2; pp. 987 - 991
• Main Authors: Wen Han, Xiao Nan-feng, Li Zhao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: Accuracy; Bayesian methods; Classification algorithms; Educational institutions; enhanced EM; feature selection; Machine learning; Mathematical model; Naïve Bayesian; Semi-supervised classification; Text categorization
• ISBN: 9781612841809; 1612841805
• DOI: 10.1109/FSKD.2011.6019690

Semi-supervised learning (SSL) based on Naïve Bayesian and Expectation Maximization (EM) combines small limited numbers of labeled data with a large amount of unlabeled data to help train classifier and increase classification accuracy. With the aim of improving the efficiency problem of the basic EM algorithm, an enhanced EM method is proposed. Firstly, a feature selection function of strong category information is constructed to control the dimension of feature vector and preserve useful feature terms. Secondly, an intermediate classifier gradually transfers unlabeled documents of maximum posterior category probability to labeled collection during each iteration process of the EM algorithm. The iteration number of the enhanced EM is obviously less than the basic EM. Finally, experiments shows that the improved method obtains very effective performance in terms of macro average accuracy and algorithm efficiency.