Evaluating feature selection strategies for high dimensional, small sample size datasets

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 949-952

  Golugula, Abhishek ^a   Lee, George ^b   Madabhushi, Anant ^b  

^a RUTGERS UNIVERSITY   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELIAC DISEASE; CLASSIFICATION ACCURACY; CORONARY HEART DISEASE; CURSE OF DIMENSIONALITY; DATA SETS; HIGH-DIMENSIONAL; K-NEAREST NEIGHBORS; KOLMOGOROV-SMIRNOV TEST; LUNG CANCER; OVARIAN CANCERS; PERFORMANCE MEASURE; PROTEIN EXPRESSIONS; RANDOM FOREST CLASSIFIER; SMALL SAMPLE SIZE; TRAINING DATA;

DECISION TREES; DISEASES; FEATURE EXTRACTION; GENE EXPRESSION; POPULATION STATISTICS; PROBABILITY DISTRIBUTIONS; TESTING;

DATA PROCESSING;

TUMOR PROTEIN;

ALGORITHM; ANIMAL; ARTICLE; AUTOMATED PATTERN RECOGNITION; DATA MINING; FACTUAL DATABASE; GENE EXPRESSION PROFILING; HUMAN; METABOLISM; METHODOLOGY; NEOPLASM; SIGNAL TRANSDUCTION;

ALGORITHMS; ANIMALS; DATA MINING; DATABASES, FACTUAL; GENE EXPRESSION PROFILING; HUMANS; NEOPLASM PROTEINS; NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; SIGNAL TRANSDUCTION;

EID: 84861931098     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6090214     Document Type: Conference Paper

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