title: Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.
abstract: This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia.
authors.forename: Luke
authors.initials: L
authors.lastname: Mathieson
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Mendes
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Marsden
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Pond
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia. Pablo.Moscato@newcastle.edu.au.
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008327:Mammography
keywords: Breast cancer diagnostics; Combinatorial optimization; Memetic algorithms; Minimum feature set; Safe data reduction