Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Applied Intelligence
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
A novel feature selection method for large-scale data sets
Intelligent Data Analysis
An efficient bit-based feature selection method
Expert Systems with Applications: An International Journal
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CBR(Case-Based Reasoning) is a problem solving technique that reuses past cases and experiences to find a solution to current problems. A critical issue in case-based reasoning is to select the correct and enough features to represent a case. However, this task is difficult to carry out since such knowledge is often exhaustively captured and cannot be represented successfully. In this paper, the new, efficient feature selection method originated from bitmap indexing and rough set techniques will be proposed. The bitmap-based feature selection method is proposed for discovering the optimal feature sets for decision-making problems. 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.