Learn\(^{++}\).MF: A random subspace approach for the missing feature problem

DOI10.1016/j.patcog.2010.05.028zbMath1209.68480OpenAlexW2124619447WikidataQ108748288 ScholiaQ108748288MaRDI QIDQ991269

Hussein Syed Mohammed, Gavin Brown, Robi Polikar, Ludmilla I. Kuncheva, Joseph DePasquale

Publication date: 2 September 2010

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2010.05.028

zbMATH Keywords

missing datarandom subspace methodensemble of classifiersmissing features

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (6)

On resilient feature selection: computational foundations of \(r\)-\(\mathbb{C} \)-reducts ⋮ Semi-supervised classification based on random subspace dimensionality reduction ⋮ A probabilistic model of classifier competence for dynamic ensemble selection ⋮ Learn++.MF ⋮ Incremental collaborative filtering based on Mahalanobis distance and fuzzy membership for recommender systems ⋮ Optimizing regression models for data streams with missing values

Uses Software

• UCI-ml
• RegEM
• AdaBoost.MH
• Learn++.NC

Cites Work

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• Bagging predictors
• Impact of imputation of missing values on classification error for discrete data
• Imputation through finite Gaussian mixture models
• A decision-theoretic generalization of on-line learning and an application to boosting
• The weight decay backpropagation for generalizations with missing values
• Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems.
• Using diversity of errors for selecting members of a committee classifier
• Robust learning with missing data

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