Machine learningEnsemble

Majority Voting

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.

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Sources

  1. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. DOI: 10.1007/BF00058655
  2. Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. link

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Referenced by

ScholarGateMajority Voting (Majority Voting Ensemble). Retrieved 2026-06-04 from https://scholargate.app/tr/ensemble-learning/majority-voting