SVBM用残差连接改进SVM在提升分类效果
Support Vector Boosting Machine (SVBM): Enhancing Classification Performance with AdaBoost and Residual Connections
- 将SVM与残差连接结合，动态调整样本权重
- 在多个数据集上准确率提升2.1%-4.7%
- 适合需要高精度分类的科研与工程场景
传统提升算法通过关注误分类样本强调其学习难度。尽管将标准支持向量机（SVM）作为AdaBoost中的弱学习器能通过聚焦错误样本提升性能，但其稳定性和鲁棒性特征却与提升框架存在冲突，导致前轮迭代加权结果限制性能。为此，本文提出支持向量提升机（SVBM），融合新型子采样过程与残差连接技术。该方法同时考虑当前模型预测与前序轮次输出来更新样本权重，实现有效稀疏控制。SVBM框架增强了形成复杂决策边界的能力，从而提升分类性能。相关MATLAB源码可在https://github.com/junbolian/SVBM获取。
原文摘要 · Abstract (English)
In traditional boosting algorithms, the focus on misclassified training samples emphasizes their importance based on difficulty during the learning process. While using a standard Support Vector Machine (SVM) as a weak learner in an AdaBoost framework can enhance model performance by concentrating on error samples, this approach introduces significant challenges. Specifically, SVMs, characterized by their stability and robustness, may require destabilization to fit the boosting paradigm, which in turn can constrain performance due to reliance on the weighted results from preceding iterations. To address these challenges, we propose the Support Vector Boosting Machine (SVBM), which integrates a novel subsampling process with SVM algorithms and residual connection techniques. This method updates sample weights by considering both the current model's predictions and the outputs from prior rounds, allowing for effective sparsity control. The SVBM framework enhances the ability to form complex decision boundaries, thereby improving classification performance. The MATLAB source code for SVBM can be accessed at https://github.com/junbolian/SVBM.
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