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Generating neural archetypes to instruct fast and interpretable decisions. (English) Zbl 1442.62766
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, 45-52 (2020).
Summary: In the field of artificial intelligence, agents learn how to take decisions by fitting their parameters on a set of samples called training set. Similarly, a core set is a subset of the training samples such that, if an agent exploits this set to fit its parameters instead of the whole training set, then the quality of the inferences does not change significantly. Relaxing the constraint that restricts the search for core sets to the available data, neural networks may be used to generate virtual samples, called archetype set, containing the same kind of information. This work illustrates the features of GH-ARCH, a recently proposed self-organizing hierarchical neural network for archetype discovery. Experiments show how the use of archetypes allows both ML agents to make fast and accurate predictions and human experts to make sense of such decisions by analyzing few important samples.
For the entire collection see [Zbl 1444.91005].
MSC:
62R07 Statistical aspects of big data and data science
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62M45 Neural nets and related approaches to inference from stochastic processes
62M20 Inference from stochastic processes and prediction
62C25 Compound decision problems in statistical decision theory
68T05 Learning and adaptive systems in artificial intelligence
Software:
Scikit
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References:
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