Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10322150 | Expert Systems with Applications | 2015 | 16 Pages |
Abstract
In our case study, interpretable hypotheses perform better than other hypotheses. DTs have a better β-error, whereas DNFs outperform DTs with respect to the α-error. We compared both hypothesis classes and exemplary hypotheses. Both are interpretable in different ways leaving the choice to preference. This leads to the conclusion that interpretable threshold based methods are appropriate for classification problems in finance. In this domain, they are not inferior to more sophisticated methods like SVMs.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lennart Obermann, Stephan Waack,