To select or to weigh: A comparative study of linear combination schemes for superparent-one-dependence estimators

IEEE Transactions on Knowledge and Data Engineering

Volumn 19, Issue 12, 2007, Pages 1652-1665

  Yang, Ying ^b   Webb, Geoffrey I ^a,b   Cerquides, Jesús ^c   Korb, Kevin B ^b   Boughton, Janice ^b   Ting, Kai Ming ^d  

^a IEEE   (Australia)

^b MONASH UNIVERSITY   (Australia)

^c UNIVERSITY OF BARCELONA   (Spain)

^d UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Bayesian probabilistic learning; Classification learning; Ensemble learning; Model selection; Model weighing; Superparent one dependence estimator (SPODE)

Indexed keywords

BAYESIAN PROBABILISTIC LEARNING; CLASSIFICATION LEARNING; ENSEMBLE LEARNING; MODEL SELECTION; MODEL WEIGHING; SUPERPARENT-ONE-DEPENDENCE ESTIMATOR (SPODE);

ALGORITHMS; BAYESIAN NETWORKS; BENCHMARKING; LARGE SCALE SYSTEMS; LEARNING SYSTEMS; STATISTICAL METHODS;

CLASSIFICATION (OF INFORMATION);

EID: 35648962940     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2007.190650     Document Type: Conference Paper

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