Abstract
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.
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References
Han, E.-H., Karypis, G., Kumar, V.: Text Categorization Using Weight Adjusted K-Nearest Neighbor Classification. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, p. 53. Springer, Heidelberg (2001)
Kurt, A., Tozal, E.: Classification of XSLT-Generated Web Documents with Support Vector Machines. In: Nayak, R., Zaki, M.J. (eds.) KDXD 2006. LNCS, vol. 3915, pp. 33–42. Springer, Heidelberg (2006)
Souafi-Bensafi, S., Parizeau, M., Lebourgeois, F., Emptoz, H.: Bayesian Networks Classifiers Applied to Documents. In: Proceeding of the 16th International Conference on Pattern Recognition, vol. 1, pp. 483–486. IEEE, Los Alamitos (2002)
de Campos, L.M., Fernandez-Luna, J.M., Huete, J.F.: A Layered Bayesian Network Model for Document Retrieval. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) ECIR 2002. LNCS, vol. 2291, pp. 169–182. Springer, Heidelberg (2002)
Wang, Y., Hodges, J., Tang, B.: Classification of Web Document using a Naïve Bayes Method. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 560–564. IEEE, Los Alamitos (2003)
Lam, W., Low, K.-F.: Automatic Document Classification Based on Probabilistic Reasoning: Model and Performance Analysis. In: International Conference on Systems, Man, and Cybernatics, vol. 3, pp. 2719–2723. IEEE, Los Alamitos (1997)
Bai, J., Nie, J.Y., Cao, G.: Integrating Compound Terms in Bayesian Text Classification. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 598–601. IEEE, Los Alamitos (2005)
The Porter Stemming Algorithm, http://www.tartarus.org/martin/PorterStemmer/
The Lancaster Stemming Algorithm, http://www.comp.lancs.ac.uk/computing/research/stemming/index.htm
The UEA-Lite Stemmer, http://www.cmp.uea.ac.uk/Research/stemmer/
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
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Khor, KC., Ting, CY. (2006). A Bayesian Approach to Classify Conference Papers. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_98
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DOI: https://doi.org/10.1007/11925231_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
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