A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Data Mining Exploration System for Feature Selection Tasks
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
A Rough Set Approach to Multiple Classifier Systems
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Inhibitory Rules in Data Analysis: A Rough Set Approach
Inhibitory Rules in Data Analysis: A Rough Set Approach
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
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In the paper two algorithms for reducts evaluation have been proposed. Presented methods use lazy algorithms to calculate the number of deterministic and inhibitory decision rules. Calculated values are used later to estimate the quality of the reducts. The two proposed algorithms have polynomial time complexity. The results obtained by both approaches were compared with performance of the two classifiers k -NN and Naive Bayesian Classifier. All algorithms were tested on several benchmark data sets from the UCI Repository of Machine Learning Databases [3].