A Bayesian Approach to Classify Conference Papers

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

, C

ognition

,

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.