Abstract
The valued tolerance relation in incomplete information systems is an important extension model of the classical rough set theory. However, the general calculation method of tolerance degree needs to know the probability distribution of an information system in advance, and it is also difficult to select a suitable threshold. In this paper, a data-driven valued tolerance relation is proposed based on the idea of data-driven data mining. The new calculation method of tolerance degree and the auto-selection method of threshold do not require any prior domain knowledge except the data set. Experiment results show that the data-driven valued tolerance relation can get better and more stable classification results than the other extension models of the classical rough set theory.
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Wang, G., Guan, L. (2012). Data-Driven Valued Tolerance Relation. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_2
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DOI: https://doi.org/10.1007/978-3-642-31900-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31899-3
Online ISBN: 978-3-642-31900-6
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