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Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Created by
Haebom
Author
Xin Du, Subramanian Ramamoorthy, Wouter Duivesteijn, Jin Tian, Mykola Pechenizkiy
Outline
This paper points out that existing machine learning models based on local decision rules are vulnerable to distributional changes and proposes a novel method leveraging causal knowledge to address this vulnerability. Specifically, we consider distributional changes in subgroups and distribution environments as a consequence of interventions in the underlying system. We introduce two regularization terms based on causal knowledge to learn and ensemble stable and optimal local decision rules. Experimental results on synthetic data and benchmark datasets demonstrate the effectiveness and robustness of the proposed method to distributional changes in diverse environments.
Takeaways, Limitations
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Takeaways:
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We present a novel method for improving the robustness of local decision rules by leveraging causal knowledge.
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It can contribute to improving the reliability of machine learning models in high-risk areas such as healthcare and finance.
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It presents a new direction for developing machine learning models that are robust to distributional changes.
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Limitations:
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The effectiveness of the proposed method may depend on specific datasets and experimental environments.
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The accuracy of causal knowledge can significantly impact results. Incomplete or incorrect causal knowledge can actually lead to poor performance.
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Further validation and experimentation are needed for application in real-world high-risk areas.
