Bandingkan kaedah

	Undian majoriti	Pengundian Berbobot
Bidang	Pembelajaran Ensemble	Pembuatan Keputusan
Keluarga	Machine learning	MCDM
Tahun asal	1996	1951
Pengasas	Leo Breiman	Arrow, K. J.
Jenis	voting aggregation	Social choice — weighted positional voting rule
Sumber perintis	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Arrow, K. J. (1951). Social Choice and Individual Values. Wiley, New York
Alias	hard voting	—
Berkaitan	5	0
Ringkasan	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.	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.