A classifier ensemble approach for the missing feature problem

Artificial Intelligence in Medicine

Volumn 55, Issue 1, 2012, Pages 37-50

  Nanni, Loris ^a   Lumini, Alessandra ^b   Brahnam, Sheryl ^c  

^a UNIVERSITY OF PADOVA   (Italy)

^b UNIVERSITY OF BOLOGNA   (Italy)

^c MISSOURI STATE UNIVERSITY   (United States)

Author keywords

Data corruption; Equipment malfunctions; Fuzzy clustering; Imputation methods; Missing values; Support vector machine

Indexed keywords

CLASSIFIER ENSEMBLES; CLUSTER-BASED; CLUSTERING APPROACH; DATA CORRUPTION; DATA IMPUTATION; DATA SETS; FEATURE SETS; IMPUTATION METHODS; MATLAB CODE; MISSING VALUES; MULTIPLE IMPUTATION; NULL HYPOTHESIS; RANDOM SUBSPACE CLASSIFIERS; RANDOM SUBSPACES; STATE-OF-THE-ART APPROACH; SUPPORT VECTOR MACHINE (SVM);

CLUSTERING ALGORITHMS; FUZZY CLUSTERING; SUPPORT VECTOR MACHINES;

AUTOCORRELATION;

ALGORITHM; ARTICLE; CLASSIFIER; CLUSTER ANALYSIS; DATA ANALYSIS; MACHINE LEARNING; NULL HYPOTHESIS; PRIORITY JOURNAL; REDUNDANCY ANALYSIS; STATISTICS; WILCOXON SIGNED RANKS TEST;

AREA UNDER CURVE; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FUZZY LOGIC; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; SUPPORT VECTOR MACHINES;

EID: 84858866778     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.11.006     Document Type: Article

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