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Communication Dans Un Congrès Année : 2021

Combining weak classifiers: a logical analysis

Résumé

A classical idea in supervised learning is to compute a strong classifier by combining weak classifiers, one of its famous implementations is the ADABOOST algorithm. We study the cost (objective function) associated to this problem in the particular case of 3 classifiers and two classes. For that purpose, an original representation of the cost, based on a truth table, is formulated as the cornerstone of this study, leading to an optimization algorithm. We prove that, under reasonable hypotheses, this cost function has a unique minimum. We study the properties of this minimum and the validity of the hypotheses. We identify directly the resulting classifier through a mathematical analysis without the need to run a numerical algorithm. The algorithm ADABOOST is not an optimization algorithm but we propose here to use an extremely close classical minimization algorithm — the relaxation algorithm — and we present an example of simulation where the relaxation provides a more convincing result.
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Dates et versions

hal-03858923 , version 1 (17-11-2022)

Identifiants

Citer

Jean-Marc Brossier, Olivier Lafitte. Combining weak classifiers: a logical analysis. SYNASC 2021 - 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, Dec 2021, Timisoara, Romania. pp.178-181, ⟨10.1109/SYNASC54541.2021.00038⟩. ⟨hal-03858923⟩
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