Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Feature selection and effective classifiers
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Applied Intelligence
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
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
Exploring high-performers' required competencies
Expert Systems with Applications: An International Journal
Data mining and preprocessing application on component reports of an airline company in Turkey
Expert Systems with Applications: An International Journal
A Boolean function approach to feature selection in consistent decision information systems
Expert Systems with Applications: An International Journal
General framework for class-specific feature selection
Expert Systems with Applications: An International Journal
Hardware-software platform for computing irreducible testors
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.