A new hybrid feature selection method based on association rules and pca for detection of breast cancer

International Journal of Innovative Computing, Information and Control

Volumn 9, Issue 2, 2013, Pages 727-729

  Inan, Onur ^a   Uzer, Mustafa Serter ^a   Yilmaz, Nihat ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Apriori; Breast cancer diagnosis; Feature selection; Neural network; PCA

Indexed keywords

10-FOLD CROSS-VALIDATION; APRIORI; ARTIFICIAL NEURAL NETWORK CLASSIFIERS; BREAST CANCER DIAGNOSIS; CLASSIFICATION ACCURACY; CROSS-VALIDATION METHODS; HYBRID FEATURE SELECTIONS; PCA;

ASSOCIATION RULES; DISEASES; FEATURE EXTRACTION; HYBRID SYSTEMS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

DIAGNOSIS;

EID: 84877825441     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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