Name
Abstract | ||
|---|---|---|
Cancer detection can be formulated as a binary classification in a machine learning paradigm. Loss functions are a critical part of almost every machine learning algorithm. While each loss function comes up with its own advantages and disadvantages, in this paper, inspired by ensemble methods, we propose a novel objective function that is a linear combination of single losses. We then integrate the proposed objective function into an Artificial Neural Network (ANN) to diagnose breast cancer. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via backpropagation. As the patients’ data are sometimes very noisy, we evaluate our method by doing comprehensive experiments on Wisconsin Breast Cancer Diagnosis (WBCD) dataset at different noise levels. The experiments show its performance declines very slowly (from 0.97 to 0.96) compared to the peer methods with the increase of noise level. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compeleceng.2020.106624 | Computers & Electrical Engineering |
Keywords | DocType | Volume |
Artificial neural network,Breast cancer detection,Margin enhancing loss function,Robust loss function | Journal | 84 |
ISSN | Citations | PageRank |
0045-7906 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamideh Hajiabadi | 1 | 0 | 0.34 |
| Vahide Babaiyan | 2 | 0 | 0.68 |
| Davood Zabihzadeh | 3 | 0 | 1.69 |
| Moein Hajiabadi | 4 | 0 | 0.34 |