Some considerations of classification for high dimension low-sample size data

Statistical Methods in Medical Research

Volumn 22, Issue 5, 2013, Pages 537-550

  Zhang, Lingsong ^a   Lin, Xihong ^b  

^a PURDUE UNIVERSITY   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

classification; consistency; discriminant analysis; machine learning; misclassification error; sparsity

Indexed keywords

ARTICLE; BAYES THEOREM; CASE CONTROL STUDY; CLASSIFICATION ALGORITHM; CLASSIFIER; COMPARATIVE STUDY; CORRELATIONAL STUDY; DATA ANALYSIS; DISCRIMINANT ANALYSIS; GENE EXPRESSION; INDEPENDENT VARIABLE; INTERNAL CONSISTENCY; LOGISTIC REGRESSION ANALYSIS; MAXIMUM LIKELIHOOD METHOD; PREDICTION; PROSTATE CANCER; SAMPLE SIZE; SIMULATION; STATISTICAL CONCEPTS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

CLASSIFICATION; CONSISTENCY; DISCRIMINANT ANALYSIS; MACHINE LEARNING; MISCLASSIFICATION ERROR; SPARSITY;

BAYES THEOREM; COMPUTER SIMULATION; HUMANS; MALE; PROSTATIC NEOPLASMS; SAMPLE SIZE; SUPPORT VECTOR MACHINES;

EID: 84886549334     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280211428387     Document Type: Article

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