| Title |
Shapley values as a strategy for ensemble weights estimation / |
| Authors |
Drungilas, Vaidotas ; Vaičiukynas, Evaldas ; Ablonskis, Linas ; Čeponienė, Lina |
| DOI |
10.3390/app13127010 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2023, vol. 13, iss. 12, art. no. 7010, p. 1-23.. ISSN 2076-3417 |
| Keywords [eng] |
machine learning ; ensemble methods ; Shapley value ; performance weighting ; privacy-preserving distributed learning |
| Abstract [eng] |
This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner's performance metric and estimates its Shapley value to measure the overall contribution of a learner to an equally weighted ensemble of various sizes. Two variants of this strategy were empirically compared with a single monolith model and other static weighting strategies using two large banking-related datasets. A variant that discards learners with a negative Shapley value was ranked as first or at least second when constructing homogeneous ensembles, whereas for heterogeneous ensembles this strategy resulted in a better or at least similar detection performance to other weighting strategies tested. The main limitation being the computational complexity of Shapley calculations, the explored weighting strategy could be considered as a generalization of performance-based weighting. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
