Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient indexing of high-dimensional data through dimensionality reduction
Data & Knowledge Engineering
MOSAIC: a fast multi-feature image retrieval system
Data & Knowledge Engineering
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Applied Intelligence
A retrieval technique for high-dimensional data and partially specified queries
Data & Knowledge Engineering
Machine Learning
Encoded Bitmap Indexing for Data Warehouses
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
The bitmap-based feature selection method
Proceedings of the 2003 ACM symposium on Applied computing
A new mutual information based measure for feature selection
Intelligent Data Analysis
Generalized rough sets based feature selection
Intelligent Data Analysis
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Selection of relevant features in a fuzzy genetic learningalgorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Feature selection is about finding useful (relevant) features to describe an application domain. The problem of finding the minimal subsets of features that can describe all of the concepts in the given data set is NP-hard. In the past, we had proposed a feature selection method, which originated from rough set and bitmap indexing techniques, to select the optimal (minimal) feature set for the given data set efficiently. Although our method is sufficient to guarantee a solution's optimality, the computation cost is very high when the number of features is huge. In this paper, we propose a nearly optimal feature selection method, called bitmap-based feature selection method with discernibility matrix, which employs a discernibility matrix to record the important features during the construction of the cleansing tree to reduce the processing time. And the corresponding indexing and selecting algorithms for such feature selection method are also proposed. Finally, some experiments and comparisons are given and the result shows the efficiency and accuracy of our proposed method.