Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth 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
An Adaptive Meta-Clustering Approach: Combining the Information from Different Clustering Results
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
An introduction to variable and feature selection
The Journal of Machine Learning Research
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Classification comparison of prediction of solvent accessibility from protein sequences
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Support Vector Machines with L1 penalty for detecting gene-gene interactions
International Journal of Data Mining and Bioinformatics
Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features
International Journal of Data Mining and Bioinformatics
A novel multi-stage feature selection method for microarray expression data analysis
International Journal of Data Mining and Bioinformatics
Hybrid feature selection through feature clustering for microarray gene expression data
International Journal of Hybrid Intelligent Systems
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A subset selected by a supervised feature selection method may not be a good one for unsupervised learning and vice versa. We propose a novel Feature Selection algorithm through Feature Clustering, FSFC. FSFC does not need the class label information in the data set and is suitable for both supervised learning and unsupervised learning. We test FSFC on some biological data sets for both clustering and classification analysis and the results indicates that FSFC algorithm can significantly reduce the original data sets without scarifying the quality of clustering and classification.