.png){kind=icon}
.png){kind=icon}
Return
An AUC-based multi-kernel weighted support vector machine ensemble algorithm for breast cancer diagnosis
.png){kind=icon}
PRE
AI
DOI：10.1080/24754269.2025.2603548.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine learning algorithms have demonstrated outstanding performance for disease diagnosis. Kernel function selection plays a crucial role in effectively transforming the nonlinear nature of input data. To enhance breast cancer diagnosis, we propose a novel ensemble algorithm, namely, AUC-Ada- $ L_{1} $ L1MKL-WSVM, which integrates Weighted Support Vector Machines (WSVM), AdaBoost, and Multi-Kernel Learning (MKL). This ensemble algorithm introduces two main innovations. First, it simultaneously updates the weights of training samples and the combined kernel function during classification. Second, it incorporates an AUC-based approach to adjust training sample weights, effectively controlling the growth rate of misclassified sample weights in AdaBoost. Experimental results are provided to demonstrate the effectiveness of our method, which achieves an AUC score of 97.21% and an accuracy of 97.64% on the WDBC dataset, and an AUC of 97.53% and an accuracy of 97.46% on the WBC dataset. Comparative analysis further confirms that our ensemble algorithm outperforms four benchmark models in classification accuracy.
Keywords:
Weighted support vector machine
breast cancer diagnosis
ensemble algorithm
multi-kernel learning
AdaBoost
Journal
S
IF:
1.3
Papers:
28
Citations:
0