Variable precision rough set model
Journal of Computer and System Sciences
Rough computational methods for information systems
Artificial Intelligence
A rough set approach to attribute generalization in data mining
Information Sciences: an International Journal
Uncertainly measures of rough set prediction
Artificial Intelligence
Rough approximation quality revisited
Artificial Intelligence
Inclusion degree: a perspetive on measures for rough set data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Approximate Reducts and Association Rules - Correspondence and Complexity Results
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Consistency-based search in feature selection
Artificial Intelligence
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis
IEEE Intelligent Systems
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute
Transactions on Rough Sets IX
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Neighborhood systems-based rough sets in incomplete information system
Knowledge-Based Systems
Incremental attribute reduction based on elementary sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Fundamenta Informaticae
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
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Many real data sets in databases may vary dynamically. With the rapid development of data processing tools, databases increase quickly not only in rows (objects) but also in columns (attributes) nowadays. This phenomena occurs in several fields including image processing, gene sequencing and risk prediction in management. Rough set theory has been conceived as a valid mathematical tool to analyze various types of data. A key problem in rough set theory is executing attribute reduction for a data set. This paper focuses on attribute reduction for data sets with dynamically-increasing attributes. Information entropy is a common measure of uncertainty and has been widely used to construct attribute reduction algorithms. Based on three representative entropies, this paper develops a dimension incremental strategy for redcut computation. When an attribute set is added to a decision table, the developed algorithm can find a new reduct in a much shorter time. Experiments on six data sets downloaded from UCI show that, compared with the traditional non-incremental reduction algorithm, the developed algorithm is effective and efficient.