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DES-AS: Dynamic ensemble selection based on algorithm Shapley
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AI
DOI：10.1016/j.patcog.2024.110899.png)
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Abstract
En 中文
Dynamic ensemble selection (DES) effectively improves aggregation performance by dynamically finding the most appropriate subset of classifiers for each query sample. The key component of this strategy is to define an appropriate criterion for evaluating the competence of the candidate classifiers. In this study, we present a new group-based criterion, synergy competence, to comprehensively measure the magnitude of the intricate synergy effect among classifiers. In addition, we introduce the algorithm Shapley, a variant of the Shapley value (SV) in the game theory, to measure the synergy competence of each classifier, which helps in the construction of a synergy-based DES system, called DES-AS. We argue that candidate classifiers with positive algorithm Shapley should be selected as they contribute to the ensemble. After selecting a subset of classifiers, the normalized algorithm Shapley is employed to calculate the voting weights for a cooperative final classification decision. The experimental analyses conducted on forty real-world datasets not only indicate that the synergy competence estimated by the algorithm Shapley is more applicable than classic group-based metrics, but also show that DES-AS is substantially more effective and robust than state-of-the-art models. The statistical test results also indicate that the estimated synergy competence reflects the real competence of each classifier better than the baseline models.
Keywords:
Dynamic ensemble selection
Classifier competence
Shapley value
Monte Carlo simulation
Dynamic weighting