Resampling methods improve the predictive power of modeling in class-imbalanced datasets

International Journal of Environmental Research and Public Health

Volumn 11, Issue 9, 2014, Pages 9776-9789

  Lee, Paul H ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Automated classifier; Data mining; Decision tree; Oversampling; Predictive power; Rare events

Indexed keywords

CLASS; CLASSIFICATION; DATA MINING; DATA SET; HEALTH SURVEY; PUBLIC HEALTH; SAMPLING; WORLD HEALTH ORGANIZATION;

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BLOOD ANALYSIS; CLASS IMBALANCED DATASET; CLASSIFICATION; CLASSIFICATION AND REGRESSION TREE; DATA ANALYSIS; DECISION TREE; DEMOGRAPHY; DIABETES MELLITUS; FEMALE; GENERALIZED BOOSTED TREE; HEALTH SURVEY; HUMAN; MALE; MIDDLE AGED; PREDICTIVE VALUE; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RESAMPLING METHOD; SOCIAL STATUS; SOCIOECONOMICS; STATISTICAL ANALYSIS; STATISTICAL CONCEPTS; STATISTICAL MODEL; WORLD HEALTH ORGANIZATION; YOUNG ADULT; DIABETES MELLITUS, TYPE 2; NUTRITION; PROCEDURES; SELF REPORT; THEORETICAL MODEL; UNITED STATES;

ADULT; AGED; DIABETES MELLITUS, TYPE 2; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, THEORETICAL; NUTRITION SURVEYS; ROC CURVE; SAMPLING STUDIES; SELF REPORT; UNITED STATES; YOUNG ADULT;

EID: 84908081464     PISSN: 16617827     EISSN: 16604601     Source Type: Journal    
DOI: 10.3390/ijerph110909776     Document Type: Article

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