arXiv:2606.29516v1 Announce Type: new Abstract: A central challenge in statistical modeling is identifying the subset of features that belong in the true regression model. The classical best subset selection problem, recently made tractable via mixed-integer optimization (MIO), finds the globally optimal sparse solution. It does not, however, make use of any information beyond the observed data. In many applied settings, domain experts can meaningfully rank or score the relevance of candidate predictors, yet no existing framework integrates such probabilistic expert assessments directly into t
The increasing complexity of AI models and the demand for interpretability are driving the need for more sophisticated feature selection techniques that can incorporate human expertise.
Integrating expert knowledge directly into statistical modeling improves model accuracy and interpretability in critical applications, reducing the 'black box' problem in AI.
Traditional best subset selection, which relies solely on data, can now be enhanced with expert-informed probabilistic assessments, leading to more robust and contextually relevant models.
- · AI researchers
- · Data scientists
- · Domain experts
- · Industries requiring interpretable AI (e.g., healthcare, finance)
- · Generic, black-box AI opaque to human understanding
Improved accuracy and trustworthiness of AI models through expert knowledge integration.
Faster development and deployment of AI systems in regulated or complex domains due to enhanced interpretability.
Reduced societal skepticism towards AI decisions as methods become more transparent and align with human understanding.
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Read at arXiv cs.LG