Feature Selection Methods for Cost-Constrained Classification in Random
Forests
- URL: http://arxiv.org/abs/2008.06298v2
- Date: Mon, 17 Aug 2020 04:25:16 GMT
- Title: Feature Selection Methods for Cost-Constrained Classification in Random
Forests
- Authors: Rudolf Jagdhuber, Michel Lang and J\"org Rahnenf\"uhrer
- Abstract summary: Cost-sensitive feature selection describes a feature selection problem, where features raise individual costs for inclusion in a model.
Random Forests define a particularly challenging problem for feature selection, as features are generally entangled in an ensemble of multiple trees.
We propose Shallow Tree Selection, a novel fast and multivariate feature selection method that selects features from small tree structures.
- Score: 3.4806267677524896
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cost-sensitive feature selection describes a feature selection problem, where
features raise individual costs for inclusion in a model. These costs allow to
incorporate disfavored aspects of features, e.g. failure rates of as measuring
device, or patient harm, in the model selection process. Random Forests define
a particularly challenging problem for feature selection, as features are
generally entangled in an ensemble of multiple trees, which makes a post hoc
removal of features infeasible. Feature selection methods therefore often
either focus on simple pre-filtering methods, or require many Random Forest
evaluations along their optimization path, which drastically increases the
computational complexity. To solve both issues, we propose Shallow Tree
Selection, a novel fast and multivariate feature selection method that selects
features from small tree structures. Additionally, we also adapt three standard
feature selection algorithms for cost-sensitive learning by introducing a
hyperparameter-controlled benefit-cost ratio criterion (BCR) for each method.
In an extensive simulation study, we assess this criterion, and compare the
proposed methods to multiple performance-based baseline alternatives on four
artificial data settings and seven real-world data settings. We show that all
methods using a hyperparameterized BCR criterion outperform the baseline
alternatives. In a direct comparison between the proposed methods, each method
indicates strengths in certain settings, but no one-fits-all solution exists.
On a global average, we could identify preferable choices among our BCR based
methods. Nevertheless, we conclude that a practical analysis should never rely
on a single method only, but always compare different approaches to obtain the
best results.
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