Multi-Relational Classification in Imbalanced Domains

Authors:
Guangmei Xu;Hong Bao;Xianyu Meng
Affiliations:
Institute of Information Technology, Beijing Union University, Beijing, China 100101;Institute of Information Technology, Beijing Union University, Beijing, China 100101;School of Computer Science & Engineering, Liaoning University of Technology, Jinzhou, China 121001
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

This paper discusses the problem of multi-relational classification on imbalanced datasets. To solve the class imbalance problem, a new multi-relational Naive Bayesian classifier named R-NB is proposed, the attribute filter criterion based on mutual information is upgraded to deal with multi-relational data directly and the basic sampling methods include under-sampling and over-sampling are adopted to eliminate or minimize rarity by altering the distribution of relational examples. Experiments show, with the help of attribute filter method, R-NB can get better results than those without that. And, experiments show that multi-relational classifiers with under-sampling methods can provide more accurate results than that with over-sampling methods considering the ROC curve.