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Making sense of economics datasets with evolutionary coresets. (English) Zbl 1442.62729
Bucciarelli, Edgardo (ed.) et al., Decision economics: complexity of decisions and decisions for complexity. Papers based on the presentations at the international conference on decision economics, DECON 2019, Ávila, Spain, June 26–28, 2019. Cham: Springer. Adv. Intell. Syst. Comput. 1009, 162-170 (2020).
Summary: 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 multi-objective 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.
For the entire collection see [Zbl 1444.91005].
62P20 Applications of statistics to economics
62H30 Classification and discrimination; cluster analysis (statistical aspects)
68T05 Learning and adaptive systems in artificial intelligence
GitHub; inspyred; Scikit
Full Text: DOI
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