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    Generalizing Gain Penalization for Feature Selection in Tree-Based Models

    Wundervald, Bruna and Parnell, Andrew and Domijan, Katarina (2020) Generalizing Gain Penalization for Feature Selection in Tree-Based Models. IEEE Access, 8. pp. 190231-190239. ISSN 2169-3536

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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.

    Item Type: Article
    Keywords: Dimensionality reduction; feature selection; gain penalization; tree-models;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15346
    Identification Number:
    Depositing User: Katarina Domijan
    Date Deposited: 25 Jan 2022 17:00
    Journal or Publication Title: IEEE Access
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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