A Comparison of Four Classification Systems Using Rule Sets Induced from Incomplete Data Sets by Local Probabilistic Approximations

Author:

Clark Patrick G.,Gao Cheng,Grzymala-Busse Jerzy W.

Publisher

Springer International Publishing

Reference21 articles.

1. Clark, P.G., Grzymala-Busse, J.W.: A comparison of classification systems for rule sets induced from incomplete data by probabilistic approximations. In: Proceedings of the ALLDATA 1-st International Conference on Big Data, Small Data, Linked Data and Open Data, pp. 46–51 (2015)

2. Grzymala-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining: Opportunities and Challenges, pp. 142–173. Idea Group Publishing, Hershey (2003)

3. Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man-Mach. Stud. 29, 81–95 (1988)

4. Wong, S.K.M., Ziarko, W.: INFER–an adaptive decision support system based on the probabilistic approximate classification. In: Proceedings of the 6-th International Workshop on Expert Systems and their Applications, pp. 713–726 (1986)

5. Yao, Y.Y.: Probabilistic rough set approximations. Int. J. Approximate Reasoning 49, 255–271 (2008)

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