Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Layered Bayesian Network Model for Document Retrieval
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Bayesian Networks Classifiers Applied to Documents
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Classification of Web Documents Using a Naive Bayes Method
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Integrating Compound Terms in Bayesian Text Classification
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Classification of XSLT-Generated web documents with support vector machines
KDXD'06 Proceedings of the First international conference on Knowledge Discovery from XML Documents
Exploiting reference section to classify paper's topics
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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This article aims at presenting a methodological approach for classifying educational conference papers by employing a Bayesian Network (BN). A total of 400 conference papers were collected and categorized into 4 major topics (Intelligent Tutoring System, Cognition, e-Learning, and Teacher Education). In this study, we have implemented a 80-20 split of collected papers. 80% of the papers were meant for keywords extraction and BN parameter learning whereas the other 20% were aimed for predictive accuracy performance. A feature selection algorithm was applied to automatically extract keywords for each topic. The extracted keywords were then used for constructing BN. The prior probabilities were subsequently learned using the Expectation Maximization (EM) algorithm. The network has gone through a series of validation by human experts and experimental evaluation to analyze its predictive accuracy. The result has demonstrated that the proposed BN has outperformed Naïve Bayesian Classifier, and BN learned from the training data.