Mayo, M., & Sun, Q. (2014). Evolving artificial datasets to improve interpretable classifiers. In 2014 IEEE Congress on Evolutionary Computation (CEC), 6-11 July 2014, Beijing, China (pp. 2367–2374). Washington, DC, USA: IEEE. http://doi.org/10.1109/CEC.2014.6900238
Permanent Research Commons link: http://hdl.handle.net/10289/8844
Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple interpretable supervised classifiers (specifically, Naive Bayes and linear Support Vector Machines) are trained (i) directly on “real” data, as would be the normal case, and (ii) indirectly, using special artificial datasets derived from real data via evolutionary optimization. The results across several challenging test problems show that supervised classifiers trained indirectly using our novel evolution-based approach produce models with superior predictive classification performance. Besides presenting the accuracy of the learned models, we also analyze the sensitivity of our artificial data optimization process to Differential Evolution's parameters, and then we examine the statistical characteristics of the artificial data that is evolved.
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