按数据特征与目标值关系分组，训练多个模型提升预测效果
Fitting Multiple Machine Learning Models with Performance Based Clustering
- 根据特征与目标的关联性对数据聚类，分别建模
- 在真实数据集上显著优于传统单模型方法
- 适用于流式数据，可动态调整模型权重
传统机器学习假设数据来自单一生成机制，但现实数据常不满足此假设，导致性能下降。本文提出一种基于特征与目标关系的聚类框架，将数据按此类关系分组，并为每组训练独立模型，以捕捉数据不同部分的规律。该框架进一步扩展至流式数据场景，通过动态更新模型集成权重来适应新数据批次。在广泛使用的实际数据集上验证，该方法显著优于传统单模型方法。
原文摘要 · Abstract (English)
Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the features and the target values and we obtain multiple separate models to learn different parts of the data. We further extend our framework to applications having streaming data where we produce outcomes using an ensemble of models. For this, the ensemble weights are updated based on the incoming data batches. We demonstrate the performance of our approach over the widely-studied real life datasets, showing significant improvements over the traditional single-model approaches.
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