Abstract
Decision-theoretic rough sets (DTRS) can be seen as a kind of misclassification cost-sensitive learning model. In DTRS, attribute reduction is the process of minimizing misclassification costs. However in parctice, data are not free, and there are test costs to obtain feature values of objects. Hence, the process of attribute reduction should help minimizing both of misclassification costs and test costs. In this paper, the minimal test cost attribute reduct (MTCAR) problem is defined in DTRS. The objective of attribute reduction is to minimize misclassification costs and test costs. A genetic algorithm (GA) is used to solve this problem. Experiments on UCI data sets are performed to validate the effectiveness of GA to solve MTCAR problem.
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Ma, X., Wang, G., Yu, H., Hu, F. (2013). Test-Cost-Sensitive Attribute Reduction in Decision-Theoretic Rough Sets. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_14
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DOI: https://doi.org/10.1007/978-3-642-44949-9_14
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