- Title
- Computer-aided breast cancer diagnosis with optimal feature sets: reduction rules and optimization techniques
- Creator
- Mathieson, Luke; Mendes, Alexandre; Marsden, John; Pond, Jeffrey; Moscato, Pablo
- Relation
- Bioinformatics, Volume 2: Structure, Function, and Applications p. 299-325
- Relation
- Methods in Molecular Biology 1526
- Publisher Link
- http://dx.doi.org/10.1007/978-1-4939-6613-4_17
- Publisher
- Springer
- Resource Type
- book chapter
- Date
- 2017
- Description
- 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 (a, ß)-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 (a, ß)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (a, ß)-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.
- Subject
- safe data reduction; combinatorial optimization; minimum feature set; breast cancer diagnostics; memetic algorithms
- Identifier
- http://hdl.handle.net/1959.13/1397515
- Identifier
- uon:34298
- Identifier
- ISBN:9781493966110
- Language
- eng
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