A Comparison of Two Versions of the MLEM2 Rule Induction Algorithm Extended to Probabilistic Approximations

Author:

Clark Patrick G.,Grzymala-Busse Jerzy W.

Publisher

Springer International Publishing

Reference20 articles.

1. Clark, P.G., Grzymala-Busse, J.W.: Experiments on probabilistic approximations. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 144–149 (2011)

2. Clark, P.G., Grzymala-Busse, J.W.: Rule induction using probabilistic approximations and data with missing attribute values. In: Proceedings of the 15-th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2012, pp. 235–242 (2012)

3. Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)

4. Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 243–250 (2002)

5. Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Workshop Notes, Foundations and New Directions of Data Mining, in Conjunction with the 3rd International Conference on Data Mining, pp. 56–63 (2003)

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1. Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm;Artificial Intelligence and Soft Computing;2017

2. A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations;Journal of Intelligent Information Systems;2015-09-22

3. A Rough Set Approach to Incomplete Data;Rough Sets and Knowledge Technology;2015

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