Rough computational methods for information systems
Artificial Intelligence
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
Various approaches to reasoning with frequency based decision reducts: a survey
Rough set methods and applications
Matrix computation for information systems
Information Sciences: an International Journal
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Fundamenta Informaticae
An introduction to variable and feature selection
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Knowledge Reduction and its Rough Entropy Representation of Decision Tables in Rough Set
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Consistency based attribute reduction
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Feature selection using rough entropy-based uncertainty measures in incomplete decision systems
Knowledge-Based Systems
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Many researchers are working on developing fast data mining methods for processing huge data sets efficiently, but some current reduction algorithms based on rough sets still have some disadvantages. In this paper, we indicated their limitations for reduct generation, then a new measure to knowledge was introduced to discuss the roughness of rough sets, and we developed an efficient algorithm for knowledge reduction based on rough sets. So, we modified the mean decision power, and proposed to use the algebraic definition of decision power. To select optimal attribute reduction, the judgment criterion of decision with an inequality was presented and some important conclusions were obtained. A complete algorithm for the attribute reduction was designed. Finally, through analyzing the given example, it is shown that the proposed heuristic information is better and more efficient than the others, and the presented method in the paper reduces time complexity and improves the performance. We report experimental results with several data sets from UCI Machine Learning Repository, and we compare the results with some other methods. The results prove that the proposed method is promising, which enlarges the application areas of rough sets.