Optimal breast cancer classification using Gauss–Newton representation based algorithm
作者：
Highlights：
• A novel GNRBA for breast cancer classification is proposed.
• It uses sparse representation with feature selection.
• It evaluates the sparsity in a computationally efficient way.
• A new Gauss-Newton based classifier is proposed.
• Experimental results on two databases of WBCD are presented.
摘要
•A novel GNRBA for breast cancer classification is proposed.•It uses sparse representation with feature selection.•It evaluates the sparsity in a computationally efficient way.•A new Gauss-Newton based classifier is proposed.•Experimental results on two databases of WBCD are presented.
论文关键词：Breast cancer classification,Sparse representation,Gauss-Newton representation based algorithm,Euclidean distance measure
论文评审过程：Received 27 December 2016, Revised 26 April 2017, Accepted 15 May 2017, Available online 15 May 2017, Version of Record 19 May 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.035
