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Probabilistic Indices of Quality of Approximation

Probabilistic Indices of Quality of Approximation

Annibal Parracho Sant’Anna
Copyright: © 2008 |Pages: 13
ISBN13: 9781599045528|ISBN10: 1599045524|ISBN13 Softcover: 9781616927448|EISBN13: 9781599045542
DOI: 10.4018/978-1-59904-552-8.ch007
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MLA

Sant’Anna, Annibal Parracho. "Probabilistic Indices of Quality of Approximation." Rough Computing: Theories, Technologies and Applications, edited by Aboul Ella Hassanien, et al., IGI Global, 2008, pp. 162-174. https://doi.org/10.4018/978-1-59904-552-8.ch007

APA

Sant’Anna, A. P. (2008). Probabilistic Indices of Quality of Approximation. In A. Hassanien, Z. Suraj, D. Slezak, & P. Lingras (Eds.), Rough Computing: Theories, Technologies and Applications (pp. 162-174). IGI Global. https://doi.org/10.4018/978-1-59904-552-8.ch007

Chicago

Sant’Anna, Annibal Parracho. "Probabilistic Indices of Quality of Approximation." In Rough Computing: Theories, Technologies and Applications, edited by Aboul Ella Hassanien, et al., 162-174. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-552-8.ch007

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Abstract

A new index of quality of approximation, called the index of mutual information, is proposed in this chapter. It measures the mutual information between the relations respectively determined by condition and decision attributes. Its computation is based on the comparison of two graphs, each one representing a set of attributes. Applications in the context of indiscernibility as well as in the context of dominance relations are considered. The combination of the new measurement approach with the transformation into probabilities of being the preferred option is also explored. A procedure to select the most important attributes is outlined. Illustrative examples are provided.

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