A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Machine learning through data classification and reduction
Fundamenta Informaticae - Special issue: intelligent information systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Evaluation of probabilistic decision tables
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Variable precision Bayesian rough set model
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Rough Set Approach to Spam Filter Learning
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Rough set-based incremental learning approach to face recognition
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Alternative rule induction methods based on incremental object using rough set theory
Applied Soft Computing
| Hi-index | 0.00 |
Rough decision tables were introduced by Pawlak in the context of rough set theory. A rough decision table represents, a non-functional in general, relationship between two groups of properties of objects, referred to as condition and decision attributes, respectively. In practical applications, the rough decision tables are normally learned from data. In this process, for better coverage of the domain of interest, they can be structured into hierarchies. To achieve convergence of the learned hierarchy of rough decision tables to a stable final state, it is desirable to avoid total regeneration of the learned structure after new objects, not represented in the hierarchy, are encountered. This can be accomplished through an incremental learning process in which the structure of rough decision tables is updated, rather than regenerated, after new observations appeared. The introduction and the investigation of this incremental learning process within the framework of the rough set model is the main theme of the article. The article is also concerned with evaluation of learned decision tables and their structures by introducing the absolute gain function to measure the quality of information represented by the tables.