Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis
Evolving clusters in gene-expression data
Information Sciences: an International Journal
A hypergraph-based approach to feature selection
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
An unsupervised feature selection framework based on clustering
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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This paper proposes a filter-based method for feature selection. The filter is based on the partitioning of the feature space into clusters of similar features. The number of clusters and, consequently, the cardinality of the subset of selected features, is automatically estimated from the data. Empirical results illustrate the performance of the proposed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to a state of the art algorithm for feature selection, but with more modest computing time requirements.