Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Decomposition of Task Specification Problems
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Data reduction: discretization of numerical attributes
Handbook of data mining and knowledge discovery
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications (Machine Perception and Artificial Intelligence)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Decomposition methodology for classification tasks: a meta decomposer framework
Pattern Analysis & Applications
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Missing template decomposition method and its implementation in rough set exploration system
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Attribute reduction is a key task for the research of rough sets. However, when dealing with large-scale data, many existing proposals based on rough set theory get worse performance. In this paper, we propose a novel attribute reduction algorithm of decomposition based on rough sets. The idea of decomposition is to break down a complex table into a super-table and several sub-tables that are simpler, more manageable and solvable by using existing induction methods, then joining them together in order to solve the original table. Compared with the traditional methods, experiments with some standard datasets from VCI database are done and experimental results illustrate that the algorithm of this paper improve computational efficiency.