We study a novel ensemble approach for feature selection based on
hierarchical stacking in cases of non-stationarity and limited number of
samples with large number of features. Our approach exploits the co-dependency
between features using a hierarchical structure. Initially, a machine learning
model is trained using a subset of features, and then the model's output is
updated using another algorithm with the remaining features to minimize the
target loss. This hierarchical structure allows for flexible depth and feature
selection. By exploiting feature co-dependency hierarchically, our proposed
approach overcomes the limitations of traditional feature selection methods and
feature importance scores. The effectiveness of the approach is demonstrated on
synthetic and real-life datasets, indicating improved performance with
scalability and stability compared to the traditional methods and
state-of-the-art approaches.

我们研究了一种新颖的集成方法，用于处理非稳态和样本有限、特征数量庞大的特征选择问题。通过利用特征之间的相互依赖关系，我们提出的层次结构方法克服了传统特征选择方法和特征重要性评分的局限性，并在合成和实际数据集上展示了可伸缩性和稳定性方面的改进性能。