基于混合堆叠模型和特征选择的水可饮性分类.
Ahmed M Elshewey1, Rasha Y Youssef2, Hazem M El-Bakry3
1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O. Box: 43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.
概括
准确的饮用水分类对于清洁水至关重要. 集体学习,特别是堆叠模型,显著提高了水质预测的准确性和可靠性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确的水质分类对于确保清洁水的获取至关重要.
- 现有的水的饮用性 (WP) 评估方法需要强大的预测模型.
- 使用了一个公开的Kaggle数据集,包括3276个水体,具有各种质量指标.
研究的目的:
- 为机器学习准备一个水可饮性数据集.
- 用优化算法识别水质分类中最重要的特征.
- 评估和比较各种机器学习分类器的性能,以预测水的饮用性.
- 通过集体学习技术,特别是堆叠来提高预测性能.
主要方法:
- 数据预处理涉及中位数归算,规范化和合成少数人过量采样技术 (SMOTE) 进行类不平衡.
- 使用二元粒子群优化 (BPSO) 和二元鱼优化算法 (BWAO) 进行特征选择 (FS),以确定关键水质指标.
- 包括随机森林 (RF),梯度提升 (GB),支持向量机 (SVM),额外树 (ET),决策树 (DT) 和XGBoost在内的多个分类器被训练和评估.
- 使用逻辑回归作为meta-learner,RF,ET和XGBoost作为基础学习者,开发了一个堆叠组合模型.
主要成果:
- BPSO确定了七个基本特征的子集,平均误差为0.3745.
- 额外树 (ET) 分类器在单个模型中实现了最高的性能,准确率为70.63%,F1得分为71.17%.
- 堆叠模型表现出更好的性能,达到69.53%的准确性,71.17%的F1得分和77.62%的AUC.
- 集体学习,特别是堆叠,在创建一个强大的水质分类框架方面被证明是有效的.
结论:
- 集体学习方法,特别是堆叠,在水可饮性分类准确性方面提供了显著的改进.
- 使用BPSO的特征选择有效地确定了关键的水质参数.
- 堆叠模型为增强水质测量和管理提供了可行的和强大的方法.
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