Making Sense of Economics Datasets with Evolutionary Coresets

Pietro Barbiero () and Alberto Tonda ()
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Pietro Barbiero: Polito - Politecnico di Torino = Polytechnic of Turin
Alberto Tonda: Université Paris-Saclay, GMPA - Génie et Microbiologie des Procédés Alimentaires - INRA - Institut National de la Recherche Agronomique - AgroParisTech

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Abstract: Machine learning agents learn to take decisions extracting information from training data. When similar inferences can be obtained using a small subset of the same training set of samples, the subset is called coreset. Coresets discovery is an active line of research as it may be used to reduce the training speed as well as to allow human experts to gain a better understanding of both the phenomenon and the decisions, by reducing the number of samples to be examined. For classification problems, the state-of-the-art in coreset discovery is EvoCore, a multiobjective evolutionary algorithm. In this work EvoCore is exploited both on synthetic and on real data sets, showing how coresets may be useful in explaining decisions taken by machine learning classifiers.

Keywords: classification; coreset discovery; EvoCore; evolutionary algorithms; explainable AI; machine learning; multi-objective (search for similar items in EconPapers)
Date: 2019-06-26
Note: View the original document on HAL open archive server: https://hal.science/hal-04244855v1
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Published in International Conference of Decision Economics, Jun 2019, Avila, Spain. pp.162-170, ⟨10.1007/978-3-030-38227-8_19⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04244855

DOI: 10.1007/978-3-030-38227-8_19

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