Supervised neural network modeling: An empirical investigation into learning from imbalanced data with labeling errors

IEEE Transactions on Neural Networks

Volumn 21, Issue 5, 2010, Pages 813-830

  Khoshgoftaar, Taghi M ^a   Van Hulse, Jason ^a   Napolitano, Amri ^a  

^a Florida Atlantic University   (United States)

Author keywords

Class imbalance; Class noise; Data sampling; Labeling errors; Neural networks

Indexed keywords

APPLICATION DOMAINS; CLASS IMBALANCE; CLASS NOISE; COMBINED EFFECT; DATA SAMPLING; EMPIRICAL INVESTIGATION; IMBALANCED DATA; MULTI-LAYER PERCEPTRONS; NEURAL NETWORK ALGORITHM; NEURAL NETWORK LEARNING ALGORITHM; NOISE DATA; PREDICTIVE PERFORMANCE; SUPERVISED NEURAL NETWORKS;

ERRORS; LABELING; MULTILAYER NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

LEARNING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; FACTUAL DATABASE; HUMAN; LEARNING; PHYSIOLOGY; RECEIVER OPERATING CHARACTERISTIC; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, FACTUAL; HUMANS; LEARNING; NEURAL NETWORKS (COMPUTER); ROC CURVE;

EID: 77951926080     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2042730     Document Type: Article

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