A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International 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 Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Task decomposition using geometric relation for min-max modular SVMs
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
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The problem of feature selection has long been an active research topic within statistics and pattern recognition. So far, most methods of feature selection focus on supervised data where class information is available. For unsupervised data, the related methods of feature selection are few. The presented article demonstrates a way of unsupervised feature selection, which is a two-level filter model removing the redundant and irrelevant features, respectively. The redundant features are eliminated using any clustering algorithm, and a new method is proposed to remove the irrelevant features: first rank the features according to their relevance to cluster and then a subset of relevant features is selected using the Fuzzy Feature Evaluation Index (FFEI) with some changes and extensions. The experimental results have shown the effectiveness of the proposed method for high-dimensional data. Our major contributions are: (1) to present a new hierarchical filter method for unsupervised feature selection; (2) to propose a new algorithm for removing the irrelevant features; (3) to extend the FFEI, and present a method for calculating the approximate weight of feature in FFEI, which improves the efficiency and robustness of the method.