Enhancing Breast Cancer Classification Using Ensemble Techniques and Feature Selection Algorithms
Author:
Affiliation:
1. Mohamed Kheider University,Dept. of Computer Science,Biskra,Algeria
2. University of Algiers 1,Faculty of Medicine,Algiers,Algeria
3. Institut FEMTO-ST, UMR CNRS 6174 - AS2M,UFC,ENSMM,Besancon,France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10536721/10536698/10536701.pdf?arnumber=10536701
Reference31 articles.
1. Optimal breast cancer classification using Gauss–Newton representation based algorithm
2. Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic
3. Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method
4. Breast cancer diagnosis using GA feature selection and Rotation Forest
5. An improved random forest-based rule extraction method for breast cancer diagnosis
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