방법 비교

	뎀프스터-샤퍼 융합	다수결 투표
분야	앙상블 학습	앙상블 학습
계열	Machine learning	Machine learning
기원 연도	1968	1996
창시자	Arthur Dempster	Leo Breiman
유형	belief fusion	voting aggregation
원전	Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30(2), 205-247.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
별칭	belief function fusion, evidence combination	hard voting
관련	2	5
요약	Dempster-Shafer fusion is an ensemble method based on evidence theory (belief functions) that combines predictions from multiple sources by assigning basic probability masses to subsets of hypotheses. Rather than requiring a probability distribution over single outcomes, it allows uncertainty over sets of outcomes, providing a richer representation of confidence and doubt. Developed by Dempster (1968) and formalized by Shafer (1976), this method is particularly useful when sources are unreliable, conflicting, or provide partial evidence.	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.