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https://hdl.handle.net/1822/76502
Título: | Feature selection optimization for breast cancer diagnosis |
Autor(es): | Antunes, Ana Rita Oliveira A. Matos, Marina Costa, Lino Rocha, Ana Maria A. C. Braga, A. C. |
Palavras-chave: | Breast cancer Feature selection Neural network Optimization Support vector machine |
Data: | 2021 |
Editora: | Springer |
Revista: | Communications in Computer and Information Science |
Citação: | Antunes A.R., Matos M.A., Costa L.A., Rocha A.M.A.C., Braga A.C. (2021) Feature Selection Optimization for Breast Cancer Diagnosis. In: Pereira A.I. et al. (eds) Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_36 |
Resumo(s): | Cancer is one of the leading causes of death in the world, which has increased over the past few years. This disease can be classified as benign or malignant. One of the first and most common cancers that appear in the human body is breast cancer, which, as the name implies, appears in the breast regardless of the person’s gender. Machine learning has been widely used to assist in the diagnosis of breast cancer. In this work, feature selection and multi-objective optimization are applied to the Breast Cancer Wisconsin Diagnostic dataset. It is intended to identify the most relevant characteristics to classify whether the diagnosis is benign or malignant. Two classifiers will be used in the feature selection task, one based on neural networks and the other on support vector machine. The objective functions to be used in the optimization process are to maximize sensitivity and specificity, simultaneously. A comparison was made between the techniques used and there was a better performance by neural networks. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/76502 |
ISBN: | 9783030918842 |
DOI: | 10.1007/978-3-030-91885-9_36 |
ISSN: | 1865-0929 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-91885-9_36 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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OL2A_2021_feature_selection.pdf | 547,08 kB | Adobe PDF | Ver/Abrir |