<ul><li>This paper introduces feature subset weighting using monotone measures for distance-based supervised learning.</li><li>The proposed method incorporates feature weights using the Choquet integral, enabling the distances to capture non-linear relationships and interactions among attributes.</li><li>An advantage of this approach is that the computed subset weights are computationally feasible, reducing the number of calculations compared to calculating all feature subset weights.</li><li>Experimental evaluation demonstrates the effectiveness of the proposed distance measure in a k-nearest neighbors classification setting.</li></ul>

Feature Subset Weighting for Distance-based Supervised Learning through Choquet Integration

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