Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
通过LOCO引导的自适应小子样本抽样进行模型无关特征选择AI 解读 ↓
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通过LOCO引导的自适应小子样本抽样进行模型无关特征选择
- 研究问题
- 如何在仅能提供预测的黑箱回归模型上直接选择重要特征，并处理高维和特征相关性带来的选择困难？
- 方法与思路
- 作者提出LAMPS，同时抽取观测与特征形成集成，用留一协变量重要性引导自适应抽样，保留探索并通过阈值选取特征。
- 材料陈述的贡献
- 摘要称基础预测模型平均训练充分时可实现高维精确特征选择，并报告模拟与真实数据中优于比较方法的表现。
- 阅读关注点
- 应核对重要特征的目标定义、平均训练充分的数学条件，以及探索机制、阈值选择和相关特征下的实验设置。
- 阅读边界
- 这是arXiv预印本，同行评审与期刊接收未经核实；摘要未列完整选择条件和比较数值，预测重要性也不能直接解释为因果效应。
原文摘要
Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging. Existing model-agnostic methods primarily estimate feature importance or conduct inference on it rather than directly selecting features, whereas many feature selection methods are model-specific or rely on the model-X assumption. We introduce LOCO-guided Adaptive Minipatch Sampling (LAMPS), a model-agnostic ensemble framework that uses any black-box regression algorithm as its base learner to select features important for predicting the response. The base learner need only produce predictions and need not perform feature selection itself. LAMPS operates within a minipatch ensemble framework that subsamples both observations and features, allowing leave-one-covariate-out (LOCO) feature importance scores to be easily computed. It adaptively concentrates minipatch sampling on features with high LOCO scores while maintaining exploration. The resulting sampling probabilities rapidly separate signal from noise features after a few iterations, enabling selection through simple thresholding. We establish that LAMPS achieves exact feature selection in high-dimensional settings, provided that the base predictive models are sufficiently well trained on average. Extensive experiments on synthetic and real data show that LAMPS outperforms state-of-the-art feature selection methods, with particularly strong performance in the presence of correlated features.