Feature selection based on a modified fuzzy C-means algorithm with supervision

DOI10.1016/S0020-0255(02)00402-4zbMath1012.68667MaRDI QIDQ1810472

Francesco Marcelloni

Publication date: 9 June 2003

Published in: Information Sciences (Search for Journal in Brave)

zbMATH Keywords

Feature selectionSupervised learningFuzzy C-means\(k\)-Nearest neighbors

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Computing methodologies and applications (68U99)

Related Items

A self-constructing fuzzy CMAC model and its applications ⋮ Sample-weighted clustering methods ⋮ Membership-margin based feature selection for mixed type and high-dimensional data: theory and applications ⋮ Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation ⋮ Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

Uses Software

• DistAl
• clusfind

Cites Work

• Cluster analysis. A survey
• Learning Boolean concepts in the presence of many irrelevant features
• Feature analysis through information granulation and fuzzy sets
• Statistical Properties of Error Estimators in Performance Assessment of Recognition Systems
• Finding Groups in Data
• A Branch and Bound Algorithm for Feature Subset Selection
• Model-Based Gaussian and Non-Gaussian Clustering
• Nearest neighbor pattern classification
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