Abstract
This paper proposes a new framework for evaluation of set-based indices based on incremental sampling. Since these indices are defined by the relations between conditional attributes (R) and decision attribute(D), incremental sampling gives four possible cases according to the increment of sets for R or D. Using this classification, the behavior of indices can be evaluated for four cases. We applied this technique to several set-based indices. The results show that the evaluation framework gives a powerful tool for evaluation of set-based indices. Especially, it is found that the behavior of indices can be determined by a firstly given dataset.
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Tsumoto, S., Hirano, S. (2013). Evaluation of Incremental Change of Set-Based Indices. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_18
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DOI: https://doi.org/10.1007/978-3-642-41299-8_18
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