Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Consistency measures for feature selection
Journal of Intelligent Information Systems
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Consistency-Based Feature Selection
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
Consistency-based feature selection is an important category of feature selection research yet is defined only intuitively in the literature. First, we formally define a consistency measure, and then using this definition, evaluate 19 feature selection measures from the literature. While only 5 of these were labeled as consistency measures by their original authors, by our definition, an additional 9 measures should be classified as consistency measures. To compare these 14 consistency measures in terms of sensitivity, we introduce the concept of quasi-linear compatibility order, and partially determine the order among the measures. Next, we propose a new fast algorithm for consistency-based feature selection. We ran experiments using eleven large datasets to compare the performance of our algorithm against INTERACT and LCC, the only two instances of consistency-based algorithms with potential real world application. Our algorithm shows vast improvement in time efficiency, while its performance in accuracy is comparable with that of INTERACT and LCC.