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On building ensembles of diverse and competent classifiers

Grant number: 22/10917-8
Support Opportunities:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): October 01, 2022
Effective date (End): September 30, 2025
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Ana Carolina Lorena
Grantee:Victor Castro Nacif de Faria
Host Institution: Divisão de Ciência da Computação (IEC). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
Associated research grant:21/06870-3 - Beyond algorithm selection: meta-learning for data and algorithm analysis and understanding, AP.JP2


Ensemble learning is a popular strategy to take advantage of classification techniques with distinct biases, by joining their competences and capabilities. This work will investigate strategies to generate ensembles of classifiers based on their competences as evaluated by a meta-learning strategy. Nonetheless, joining different classifiers is profitable only when their errors are uncorrelated, so that the errors of a set of classifiers can be compensated by the correct predictions of an- other set. Therefore, measures of diversity of the classifiers' predictions will also be considered. (AU)

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