Balanced gradient boosting from imbalanced data for clinical outcome prediction

Statistical Applications in Genetics and Molecular Biology

Volumn 8, Issue 1, 2009, Pages

  Teramoto, Reiji ^a  

^a NEC CORPORATION   (Japan)

Author keywords

Boosting; Cancer; Clinical outcome; Cost sensitive learning; Diabetes; Diagnosis; Ensemble learning; Imbalanced data; Renal cell carcinoma

Indexed keywords

MICRORNA; TUMOR MARKER;

ACCURACY; ALGORITHM; ARTICLE; CANCER GRADING; DIABETIC PATIENT; GENE EXPRESSION; HUMAN; KIDNEY CARCINOMA; OUTCOME ASSESSMENT; PROGNOSIS; RANDOM FOREST; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; CANCER STAGING; DIABETES MELLITUS; EVALUATION; GENE EXPRESSION PROFILING; GENETICS; KIDNEY TUMOR; METHODOLOGY; MORTALITY; NEOPLASM; PATHOLOGY; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS; SURVIVAL;

ALGORITHMS; CARCINOMA, RENAL CELL; DATA INTERPRETATION, STATISTICAL; DIABETES MELLITUS; GENE EXPRESSION PROFILING; HUMANS; KIDNEY NEOPLASMS; MICRORNAS; MODELS, STATISTICAL; NEOPLASM STAGING; NEOPLASMS; PROGNOSIS; REPRODUCIBILITY OF RESULTS; SURVIVAL ANALYSIS;

EID: 67249139015     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.2202/1544-6115.1422     Document Type: Article

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