Building semantic digital libraries: automated ontology linking by associative naïve bayes classifier

Authors:
Hyunki Kim;Myung-Gil Jang;Su-Shing Chen
Affiliations:
Computer and Information Science and Engineering Department, Electronics and Telecommunications Research Institute, Taejon, Republic of Korea;Computer and Information Science and Engineering Department, Electronics and Telecommunications Research Institute, Taejon, Republic of Korea;Computer and Information Science and Engineering Department, University of Florida, Gainesville, Florida
Venue:
ECDL'05 Proceedings of the 9th European conference on Research and Advanced Technology for Digital Libraries
Year:
2005

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Extending naïve Bayes classifiers using long itemsets

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning

Machine Learning

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Abstract

In this paper, we present a new classification method, called Associative Naïve Bayes (ANB), to associate MEDLINE citations with Gene Ontology (GO) terms. We define the concept of class-support to find frequent itemsets and the concept of class-all-confidence to find interesting itemsets. Empirical test results on three MEDLINE datasets show that ANB is superior to naïve Bayesian classifier. The results also show that ANB outperforms the state of the art Large Bayes classifier.