A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Representation and recognition in vision
Representation and recognition in vision
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Unifying the error-correcting and output-code AdaBoost within the margin framework
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
New clustering methods for interval data
Computational Statistics
Exploitation of Multivalued Type Proximity for Symbolic Feature Selection
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Fuzzy feature selection based on min-max learning rule and extension matrix
Pattern Recognition
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
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In this paper we propose a feature selection method for symbolic interval data based on similarity margin. In this method, classes are parameterized by an interval prototype based on an appropriate learning process. A similarity measure is defined in order to estimate the similarity between the interval feature value and each class prototype. Then, a similarity margin concept has been introduced. The heuristic search is avoided by optimizing an objective function to evaluate the importance (weight) of each interval feature in a similarity margin framework. The experimental results show that the proposed method selects meaningful features for interval data. In particular, the method we propose yields a significant improvement on classification task of three real-world datasets.