方法对比

	多数表决	AdaBoost
领域	集成学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1996	1997
提出者	Leo Breiman	Freund, Y. & Schapire, R.E.
类型	voting aggregation	Ensemble (sequential boosting of weak learners)
开创性文献	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.
别名	hard voting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
相关	5	5
摘要	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.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.