A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
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Artificial Intelligence
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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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Consistency-based search in feature selection
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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Data Mining and Knowledge Discovery
Fuzzy feature selection based on min-max learning rule and extension matrix
Pattern Recognition
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ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Feature selection with dynamic mutual information
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Local Kernel Regression Score for Selecting Features of High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
Pitfalls of supervised feature selection
Bioinformatics
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Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stock trend prediction based on fractal feature selection and support vector machine
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ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Information Sciences: an International Journal
IEEE Internet Computing
Feature selection via dependence maximization
The Journal of Machine Learning Research
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
On Similarity Preserving Feature Selection
IEEE Transactions on Knowledge and Data Engineering
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A Spatial Distance Join (SDJ) based feature selection method (SDJ-FS) is developed to extend the concept of Correlation Fractal Dimension (CFD) to handle both feature relevance and redundancy jointly for supervised feature selection problems. The Pair-count Exponents (PCEs) for the SDJ between different classes and that of the entire dataset (i.e., the CFD of the dataset) are proposed respectively as feature relevance and redundancy measures. For the SDJ-FS method, an efficient divide-count approach of backward elimination property is designed for the calculation of the SDJ based feature quality (relevance and redundancy) measures. The extensive evaluations on both synthetic and benchmark datasets demonstrate the capability of SDJ-FS in identification of feature subsets of high relevance and low redundancy, along with the favorable performance of SDJ-FS over other reference feature selection methods (including those based on CFD). The success of SDJ-FS shows that, SDJ provides a good framework for the extension of CFD to supervised feature selection problems and offers a new view point for feature selection researches.