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An explainable and interpretable composite indicator based on decision rules
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AI
DOI：10.1016/j.omega.2026.103513.png)
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Abstract
En 中文
• A novel framework for constructing explainable and interpretable composite indicators using if–then decision rules is proposed. • To induce the rules from scored or classified units, the Dominance-based Rough Set Approach is applied. • A new algorithm that efficiently induces all minimal rules in a single run is presented. • An original procedure selects a consistent and minimal subset of decision rules from all possible rules derived from classification data. • The methodology is adapted to handle datasets with missing values.