TY - GEN
T1 - Disjunctive naïve Bayesian classifier to enhance accuracy for dynamic prediction
AU - Faisal Kabir, Md
AU - Chowdury, Hossain Ashik Mahmud
AU - Dahal, Keshav
AU - Hossain, Alamgir
AU - Rahman, Chowdhury Mofizur
N1 - Funding Information:
This research has been supported by the EU Erasmus Mundus Project - eLINK (east-west Link for Innovation, Networking and Knowledge exchange) under External Cooperation Window – Asia Regional Call (EM ECW – ref. 149674-EM-1-2008-1-UK-ERAMUNDUS).
Funding Information:
This research has been supported by the EU Erasmus Mundus Project - eLINK (east-west Link for Innovation, Networking and Knowledge exchange) under External Cooperation Window - Asia Regional Call (EM ECW - ref. 149674-EM-1-2008-1-UK-ERAMUNDUS).
Publisher Copyright:
© 2010 SKIMA Organizing Committee.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - A novel supervised learning algorithm named disjunctive naïve Bayesian classifier is presented in this paper. In conventional naïve Bayesian classifier only one set of class conditional probabilities are calculated from the given data. However, in our proposed approach we divide the data using k-means clustering -and save the center of each cluster. We then train these k clusters in naïve Bayesian classifier. For a new case to classify we compute similarity with the previously obtained cluster centers and based on the best match, we use the appropriate cluster set conditional probability to predict the class. We tested our proposed model on a number of benchmark data and attained higher classification accuracy rates than conventional naïve Bayesian classifier.
AB - A novel supervised learning algorithm named disjunctive naïve Bayesian classifier is presented in this paper. In conventional naïve Bayesian classifier only one set of class conditional probabilities are calculated from the given data. However, in our proposed approach we divide the data using k-means clustering -and save the center of each cluster. We then train these k clusters in naïve Bayesian classifier. For a new case to classify we compute similarity with the previously obtained cluster centers and based on the best match, we use the appropriate cluster set conditional probability to predict the class. We tested our proposed model on a number of benchmark data and attained higher classification accuracy rates than conventional naïve Bayesian classifier.
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M3 - Conference contribution
AN - SCOPUS:85089378854
T3 - SKIMA 2010 - Proceedings of the 4th International Conference on Software, Knowledge, Information Management and Applications: ''Towards Happiness and Sustainable Development''
SP - 265
EP - 269
BT - SKIMA 2010 - Proceedings of the 4th International Conference on Software, Knowledge, Information Management and Applications
A2 - Tonmukayakul, Ouyporn
A2 - Songkroh, Manawin
A2 - Sureephong, Pradorn
PB - SKIMA
T2 - 4th International Conference on Software, Knowledge, Information Management and Applications: Towards Happiness and Sustainable Development, SKIMA 2010
Y2 - 25 August 2010 through 27 August 2010
ER -