Chapter title |
Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.
|
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Chapter number | 17 |
Book title |
Bioinformatics
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Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6613-4_17 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6611-0, 978-1-4939-6613-4
|
Authors |
Luke Mathieson, Alexandre Mendes, John Marsden, Jeffrey Pond, Pablo Moscato |
Editors |
Jonathan M. Keith |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 15 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 3 | 20% |
Professor > Associate Professor | 2 | 13% |
Researcher | 2 | 13% |
Professor | 1 | 7% |
Lecturer | 1 | 7% |
Other | 1 | 7% |
Unknown | 5 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 3 | 20% |
Medicine and Dentistry | 2 | 13% |
Environmental Science | 1 | 7% |
Sports and Recreations | 1 | 7% |
Chemistry | 1 | 7% |
Other | 0 | 0% |
Unknown | 7 | 47% |