样本大小对MLP优化算法的影响,用于预测客户订阅期限存款的预测
Tshegofatso Botlhoko1, Tlhalitshi Volition Montshiwa1
1Department of Business Statistics & Operations Research, North West University Faculty of Economic and Management Sciences, Potchefstroom, North West, 2735, South Africa.
F1000Research
|February 19, 2026
概括
共变矩阵适应进化策略-多层感知 (CMA-ES-MLP) 提供最佳的分类准确性和效率. 在这项机器学习研究中,样本大小的变化并没有持续影响算法性能.
科学领域:
- 机器学习 机器学习
- 优化算法 优化算法
- 数据科学数据科学数据科学
背景情况:
- 多层感知器 (MLP) 面临着参数增长和高维空间信息丢失的挑战.
- 优化算法旨在确定最佳的MLP参数,解决这些局限性.
研究的目的:
- 对比基因算法 (GA),草优化算法 (GOA) 和共变矩阵适应进化策略 (CMA-ES) 的性能,以优化MLP.
- 研究不同样本大小对这些优化算法的影响.
- 确定对分类任务最有效的MLP优化策略.
主要方法:
- 使用来自葡萄牙银行机构 (UCI机器学习库) 的直接营销数据集.
- 应用合成少数群体过量采样技术 (SMOTE) 来平衡训练数据中的二元依赖变量.
- 根据分类准确性,特异性,敏感性,精度,F-score和执行时间评估GA,GOA和CMA-ES.
主要成果:
- 使用CMA-ES (CMA-ES-MLP) 优化的MLP在关键指标上表现出卓越的整体分类性能.
- CMA-ES-MLP实现了高分类准确度,特异性,灵敏度,精度和F分数.
- 在训练执行时间方面,CMA-ES-MLP是第二快的算法.
结论:
- 在样本大小和分类性能之间没有观察到一致的趋势;指标波动.
- CMA-ES-MLP,特别是培训样本大小为5114,被认为是理想的分类器.
- 未来的研究应该在相同的实验条件下将CMA-ES-MLP与其他高性能分类器进行比较.
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