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Evolving artificial datasets to improve interpretable classifiers
Abstract
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.
Type
Conference Contribution
Type of thesis
Series
Citation
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
Date
2014
Publisher
IEEE
Degree
Supervisors
Rights
©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.