Application of Normalized Decision Measures to the New Case Classification

Authors:
Dominik Slezak;Jakub Wroblewski
Affiliations:
-;-
Venue:
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Year:
2000

Citing 5
Cited 4

Rough membership functions

Advances in the Dempster-Shafer theory of evidence
Machine learning, neural and statistical classification

Machine learning, neural and statistical classification
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Decision Making with Probabilistic Decision Tables

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis

Fundamenta Informaticae

Entropies and Co-Entropies of Coverings with Application to Incomplete Information Systems

Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Flexible Indiscernibility Relations for Missing Attribute Values

Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Approximate Entropy Reducts

Fundamenta Informaticae
Ensembles of Classifiers Based on Approximate Reducts

Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)

Quantified Score

Hi-index	0.00

Visualization

Abstract

The optimization of rough set based classification models with respect to parameterized balance between a model's complexity and confidence is discussed. For this purpose, the notion of a parameterized approximate inconsistent decision reduct is used. Experimental extraction of considered models from real life data is described.