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| スタックド一般化（Stacked Generalization）× | 加重投票× | |
|---|---|---|
| 分野≠ | アンサンブル学習 | 意思決定 |
| 系統≠ | Machine learning | MCDM |
| 提唱年≠ | 1992 | 1951 |
| 提唱者≠ | David Wolpert | Arrow, K. J. |
| 種類≠ | meta-learning aggregation | Social choice — weighted positional voting rule |
| 原典≠ | Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259. DOI ↗ | Arrow, K. J. (1951). Social Choice and Individual Values. Wiley, New York DOI ↗ |
| 別名≠ | stacking, meta-learning | — |
| 関連≠ | 3 | 0 |
| 概要≠ | Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models. | WEIGHTED-VOTING (Weighted Voting — Weighted positional aggregation of multiple rankings) is a ranking multi-criteria decision-making (MCDM) method introduced by Arrow, K. J. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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