- author = "Babak Hodjat and Hormoz Shahrzad and Risto Miikkulainen and Lawrence Murray and Chris Holmes",
- title = "{PRETSL}: Distributed Probabilistic Rule Evolution for Time-Series Classification",
- booktitle = "Genetic Programming Theory and Practice XIV",
- year = "2016",
- editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier",
- pages = "139--148",
- address = "Ann Arbor, USA",
- month = "19-21 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Probabilistic Rule-sets, Distributed Processing, Time Series Classification",
- isbn13 = "978-3-319-97087-5",
-
URL = "
http://nn.cs.utexas.edu/?hodjat:gptp16",
-
URL = "
http://nn.cs.utexas.edu/downloads/papers/hodjat.gptp16.pdf",
-
URL = "https://www.springer.com/us/book/9783319970875",
-
DOI = "
doi:10.1007/978-3-319-97088-2_9",
- size = "12 pages",
- abstract = "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.",
-
notes = "
Part of \cite{Tozier:2016:GPTP} tpublished after the workshop",
