Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms-Reference-Cited by-同舟云学术

Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms

Published:2010 Issue: Volume: Page:130-160
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Sikora Marek

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-11479-3_8.pdf

Reference61 articles.

1. Agotnes, T.: Filtering large propositional rule sets while retaining classifier performance. MSc Thesis. Norwegian University of Science and Technology, Trondheim, Norway (1999)

2. Lecture Notes in Artificial Intelligence;T. Agotnes,1999

3. An, A., Cercone, N.: Rule quality measures for rule induction systems – description and evaluation. Computational Intelligence 17, 409–424 (2001)

4. Lecture Notes in Computer Science;J. Bazan,2006

5. Bazan, J.: A comprasion of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methododology and Applications, pp. 321–365. Physica, Heidelberg (1998)

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