Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy.

Kuncheva, L. I., & Whitaker, C. J. (2003). Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning, 51(2), 181-207.

Type

Journal article

Intellectual base

Primary research

Year

2003

Volume

51

Issue

2

Pages

181-207

DOI

10.1023/a:1022859003006

DOI registrant

Springer Nature

Link

https://doi.org/10.1023/A:1022859003006

Cited from

statistics_experimental_design

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Correlated-Source Attribution Failure
  • Ensemble forecasting: an ensemble of similarly trained models has effective size smaller than its count; per-model feature attributions are weaker than per-model importance scores suggest.

    This sourceEstablishes that correlated (low-diversity) base learners give an ensemble effective size smaller than its count, weakening the independent contribution attributable to each member. (Resolves needs-source-854; supports correlated base learners reducing effective ensemble size and per-model attribution.)

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