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| 多数決 (Majority Voting)× | スタックド一般化（Stacked Generalization）× | |
|---|---|---|
| 分野 | アンサンブル学習 | アンサンブル学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1996 | 1992 |
| 提唱者≠ | Leo Breiman | David Wolpert |
| 種類≠ | voting aggregation | meta-learning aggregation |
| 原典≠ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. DOI ↗ | Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259. DOI ↗ |
| 別名≠ | hard voting | stacking, meta-learning |
| 関連≠ | 5 | 3 |
| 概要≠ | Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy. | 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. |
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