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Bioinformatics pp 299–325Cite as

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

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.

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Notes

  1. 1.

    http://www.csc.lsu.edu/trianta/ResearchAreas/DigitalMammography/index.html.

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Authors and Affiliations

  1. Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Luke Mathieson, Alexandre Mendes, John Marsden, Jeffrey Pond & Pablo Moscato

Authors
  1. Luke Mathieson
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  2. Alexandre Mendes
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  3. John Marsden
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  4. Jeffrey Pond
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  5. Pablo Moscato
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Corresponding author

Correspondence to Pablo Moscato .

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Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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Mathieson, L., Mendes, A., Marsden, J., Pond, J., Moscato, P. (2017). Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_17

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_17

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