Ensemble Feature Selection for Breast Cancer Classification using Microarray Data
Keywords:Ensemble approach, Feature selection, Microarray data, Genetic Algorithm
For breast cancer data classification, we propose an ensemble filter feature selection approach named â€˜EnSNRâ€™. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification â€˜modelâ€™. The efficiency of the â€˜modelâ€™ is validated using 10-Fold Cross-Validation re-sampling. The Microarray dataset used in our experiments contains 50,739 genes for each of 32 patients. When our proposed â€˜EnSNRâ€™ subset of features is used; as well as giving an enhanced degree of prediction accuracy and reducing the number of irrelevant features (genes), there is also a small saving of computer processing time.
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Copyright (c) 2020 Iberamia & The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Inteligencia Artificial (Ed. IBERAMIA)
ISSN: 1988-3064 (on line).
(C) IBERAMIA & The Authors