A comparison of rule sets induced by techniques based on rough set theory

The main goal of this paper is to investigate the performance of rule sets obtained by different techniques based on the rough set theory. The performance of a rule set is defined as its ability to classify a set of objects into predefined classes. The set of rules contains the if then form rules. The set of objects is represented by flat table (table, organized data) where each object is represented by its attributes. To generate the rule sets we have used Rosetta software system. The performance of rule sets is measured by employment of the confusion matrix.

[1]  V. Brtka,et al.  A Comparison of Rule Sets Generated from Databases by Indiscernibility Relation - A Rough Sets Approach , 2007, 2007 IEEE International Conference on Intelligent Computer Communication and Processing.

[2]  David S. Johnson,et al.  Approximation algorithms for combinatorial problems , 1973, STOC.

[3]  V. Brtka,et al.  Automated Extraction of Decision Rules from Medical Databases - A Rough Sets Approach , 2007, 2007 5th International Symposium on Intelligent Systems and Informatics.

[4]  Jan Komorowski,et al.  Modelling prognostic power of cardiac tests using rough sets , 1999, Artif. Intell. Medicine.

[5]  Aleksander Øhrn,et al.  Discernibility and Rough Sets in Medicine: Tools and Applications , 2000 .

[6]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[7]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[8]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[9]  Z. Pawlak,et al.  Rough set approach to multi-attribute decision analysis , 1994 .

[10]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.