Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Managing Uncertainty in Expert Systems
Managing Uncertainty in Expert Systems
Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Modelling Medical Diagnostic Rules Based on Rough Sets
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Approximation Region-Based Decision Tables
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Decision Rules, Bayes' Rule and Ruogh Sets
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Intelligent data analysis
Rough Sets: Trends, Challenges, and Prospects
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Importance and Interaction of Conditions in Decision Rules
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
The rough set exploration system
Transactions on Rough Sets III
Variable precision Bayesian rough set model and its application to Kansei engineering
Transactions on Rough Sets V
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
A Controller Design for the Khepera Robot: A Rough Set Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
| Hi-index | 0.00 |
Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm can be associated. It is shown that every decision algorithm reveals some well known probabilistic properties, in particular it satisfies the Total Probability Theorem and the Bayes' Theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.