Computer-aided breast cancer diagnosis with optimal feature sets: Reduction rules and optimization techniques
- Publication Type:
- Chapter
- Citation:
- Methods in Molecular Biology, 2017, 1526 pp. 299 - 325
- Issue Date:
- 2017-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Pages from 2017_Book_Bioinformatics_Luke.pdf | Published version | 354.05 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© Springer Science+Business Media New York 2017. 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.
Please use this identifier to cite or link to this item: