The distributed evolutionary computation platform EC-Star is extended in this paper to probabilistic classifiers. This extension, called PRETSL, allows the distributed age-layered evolution of probabilistic rule sets, which in turn makes more fine-grained decisions possible. The method is tested on 20 UCI data problems, as well as a larger dataset of arterial blood pressure waveforms. The results show consistent improvement in all cases compared to binary classification rule-sets. Probabilistic rule evolution is thus a promising approach to difficult classification tasks and particularly well suited for time-series classification.

This work was done at Sentient Technologies, Inc. and University of Oxford.

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In Genetic Programming Theory and Practice XIV, New York, 2018. Springer.

@incollection{hodjat:gptp18, title={PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification}, author={Babak Hodjat and Hormoz Shahrzad and Risto Miikkulainen and Lawrence Murray and Chris Holmes}, booktitle={Genetic Programming Theory and Practice XIV}, address={New York}, publisher={Springer}, url="http://nn.cs.utexas.edu/?hodjat:gptp18", year={2018} }