Ensemble Feature Selection for Breast Cancer Classification using Microarray Data

Authors

  • Supoj Hengpraprohm Nakhon Pathom Rajabhat University
  • Suwimol Jungjit Thaksin University

Keywords:

Ensemble approach, Feature selection, Microarray data, Genetic Algorithm

Abstract

For breast cancer data classification, we propose an ensemble filter feature selection approach named ‘EnSNR’. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification ‘model’. The efficiency of the ‘model’ 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 ‘EnSNR’ 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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Published

2020-07-13

How to Cite

Hengpraprohm, S., & Jungjit, S. (2020). Ensemble Feature Selection for Breast Cancer Classification using Microarray Data. Inteligencia Artificial, 23(65), 100-114. Retrieved from https://journal.iberamia.org/index.php/intartif/article/view/448