Clustering-based ensembles for one-class classification

DOI10.1016/j.ins.2013.12.019zbMath1335.68205OpenAlexW2061237952WikidataQ62630395 ScholiaQ62630395MaRDI QIDQ278701

Bogusław Cyganek, Michał Woźniak, Bartosz Krawczyk

Publication date: 2 May 2016

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

Full work available at URL: https://doi.org/10.1016/j.ins.2013.12.019

zbMATH Keywords

clusteringsoft computingclassifier ensemblemultiple classifier systemone-class classification

Mathematics Subject Classification ID

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

Related Items (6)

Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification ⋮ Detecting outliers for complex nonlinear systems with dynamic ensemble learning ⋮ Clustering via fuzzy one-class quadratic surface support vector machine ⋮ Selective ensemble of SVDDs with Renyi entropy based diversity measure ⋮ RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification ⋮ Proximal gradient method for huberized support vector machine

Uses Software

• R
• SHOGUN
• LIBSVM
• Kernlab
• e1071

Cites Work

• Support vector data description
• Soft clustering using weighted one-class support vector machines
• Limits on the majority vote accuracy in classifier fusion
• One-class support vector ensembles for image segmentation and classification
• Notes on a Linguistic Description as the Basis for Automatic Image Understanding
• A FAST k-MEANS IMPLEMENTATION USING CORESETS
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