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Proceedings/Recueil Des Communications Proceedings of Machine Learning Research Année : 2023

Sample Boosting Algorithm (SamBA) -An Interpretable Greedy Ensemble Classifier Based On Local Expertise For Fat Data

Résumé

Ensemble methods are a very diverse family of algorithms with a wide range of applications. One of the most commonly used is boosting, with the prominent Adaboost. Adaboost relies on greedily learning base classifiers that rectify the error from previous iterations. Then, it combines them through a weighted majority vote, based on their quality on the entire learning set. In this paper, we propose a supervised binary classification framework that propagates the local knowledge acquired during the boosting iterations to the prediction function. Based on this general framework, we introduce SamBA, an interpretable greedy ensemble method designed for fat datasets, with a large number of dimensions and a small number of samples. SamBA learns local classifiers and combines them, using a similarity function, to optimize its efficiency in data extraction. We provide a theoretical analysis of SamBA, yielding convergence and generalization guarantees. In addition, we highlight SamBA's empirical behavior in an extensive experimental analysis on both real biological and generated datasets, comparing it to state-of-the-art ensemble methods and similarity-based approaches.
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Dates et versions

hal-04222495 , version 1 (29-09-2023)

Identifiants

  • HAL Id : hal-04222495 , version 1

Citer

Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, et al.. Sample Boosting Algorithm (SamBA) -An Interpretable Greedy Ensemble Classifier Based On Local Expertise For Fat Data. Proceedings of Machine Learning Research, 216, pp.130-140, 2023, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. ⟨hal-04222495⟩
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