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Generalizing Gain Penalization for Feature Selection in Tree-Based Models
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DOI：10.1109/ACCESS.2020.3032095.png)
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摘要
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We develop a new approach for feature selection via gain penalization in tree-based models. First, we show that previous methods do not perform sufficient regularization and often exhibit sub-optimal out-of-sample performance, especially when correlated features are present. Instead, we develop a new gain penalization idea that exhibits a general local-global regularization for tree-based models. The new method allows for full flexibility in the choice of feature-specific importance weights, while also applying a global penalization. We validate our method on both simulated and real data, exploring how the hyperparameters interact and we provide the implementation as an extension of the popular R package ranger.
Keyword:
Dimensionality reduction
feature selection
gain penalization
tree-models
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IF:
3.6
论文数:
9.8W
被引数:
29.4W
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