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.
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Xu, G., Bao, H., Meng, X. (2008). Multi-Relational Classification in Imbalanced Domains. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_62
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DOI: https://doi.org/10.1007/978-3-540-92137-0_62
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