Ensemble Methods for Combining Classification Models in MQL5

Summary

This article presents ways to combine classifiers whose outputs may be discrete class choices, confidence values, probabilities, or ordinal rankings. It explains why rankings can help compare models with differently scaled outputs and why they are more informative when there are many possible classes. The methods include majority voting, which selects the most-voted class, and Borda count, which aggregates each model’s relative ordering. It also discusses alternative objectives that can reduce a candidate class set or rank classes within that set.

The article describes an MQL5 implementation and reports demo error values for several ensemble approaches, including pairwise, intersection, and union variants. These results provide an example comparison, but the supplied text does not give enough information to assess the data split, baseline, or generalization. Majority voting is simple but discards runner-up information and weights all models equally; tie handling and model-output interpretation also matter. The examples are methodological demonstrations, not evidence that a given ensemble will improve live trading decisions.

Key ideas

• Classification ensembles can combine hard labels, scores, probabilities, or ranked class outputs.
• Majority voting selects the class receiving the most component-model votes.
• Borda count uses models’ full class rankings rather than only their top choices.
• Rank conversion can reduce scaling conflicts between heterogeneous models.
• Ensemble comparisons require caution because demo errors alone do not establish out-of-sample performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.