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-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.

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To cite this article: Mushuang Cheng, Lintong Liu, Haixiang Lin & Guoqiang Wang (22 Dec 2025): An AUC-based multi-kernel weighted support vector machine ensemble algorithm for breast cancer diagnosis, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2025.2603548

To link to this article: https://doi.org/10.1080/24754269.2025.2603548