用模拟退火优化随机森林超参数，提升分类准确率。
Feature Importance Guided Random Forest Learning with Simulated Annealing Based Hyperparameter Tuning
- 基于特征重要性采样，聚焦关键特征进行学习。
- 在信用风险、医疗诊断等任务中准确率显著提升。
- 适合需要高精度与可解释性的实际应用。
本文提出一种新框架，通过概率特征采样与模拟退火算法优化随机森林超参数，显著提升分类模型的预测准确率与泛化能力。该方法有效应对信用风险评估、物联网异常检测、早期医学诊断及高维生物数据分析等多领域挑战。通过强化对数据中关键信号的捕捉，并实现自适应超参数配置，模型能更精准地识别贡献度高的特征。实验表明，该框架在多个任务中均取得一致性能提升，并提供关于特征重要性的深入洞察，验证了重要性感知采样与元启发式优化结合的有效性。
原文摘要 · Abstract (English)
This paper introduces a novel framework for enhancing Random Forest classifiers by integrating probabilistic feature sampling and hyperparameter tuning via Simulated Annealing. The proposed framework exhibits substantial advancements in predictive accuracy and generalization, adeptly tackling the multifaceted challenges of robust classification across diverse domains, including credit risk evaluation, anomaly detection in IoT ecosystems, early-stage medical diagnostics, and high-dimensional biological data analysis. To overcome the limitations of conventional Random Forests, we present an approach that places stronger emphasis on capturing the most relevant signals from data while enabling adaptive hyperparameter configuration. The model is guided towards features that contribute more meaningfully to classification and optimizing this with dynamic parameter tuning. The results demonstrate consistent accuracy improvements and meaningful insights into feature relevance, showcasing the efficacy of combining importance aware sampling and metaheuristic optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。