Selection-fusion approach for classification of datasets with missing values

Pattern Recognition

Volumn 43, Issue 6, 2010, Pages 2340-2350

  Ghannad Rezaie, Mostafa ^a,b,c   Soltanian Zadeh, Hamid ^a,d   Ying, Hao ^b   Dong, Ming ^b  

^a HENRY FORD HOSPITAL   (United States)

^b Wayne State University   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

^d UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Ensemble classifiers; Missing value management; Multiple imputations; Pruning; Subspace classifiers; Support vector machine (SVM)

Indexed keywords

CLASSIFICATION ACCURACY; CLUSTERING PROBLEMS; DATA SETS; DETROIT; ENSEMBLE CLASSIFIERS; HENRY FORD; INCOMPLETE DATA; MATHEMATICAL FRAMEWORKS; MICHIGAN; MISSING VALUES; MULTIPLE IMPUTATION; NEW APPROACHES; NUMBER OF SAMPLES; NUMERICAL CRITERIA; PATTERN DISCOVERY; SUBSET SELECTION; SUBSPACE CLASSIFIER; TRADE OFF; UNIVERSITY OF CALIFORNIA;

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTATIONAL COMPLEXITY; IMAGE RETRIEVAL; LOCOMOTIVES; RAILROAD CARS; SET THEORY; SUPPORT VECTOR MACHINES;

AUTOCORRELATION;

EID: 76749163647     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.12.003     Document Type: Article

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