Benchmarking a Coevolutionary Streaming Classifier under the Individual Household Electric Power Consumption Dataset

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Loginov:2016:IJCNN,
  author =       "Alexander Loginov and Malcolm I. Heywood and 
                 Garnett Wilson",
  title =        "Benchmarking a Coevolutionary Streaming Classifier
                 under the Individual Household Electric Power
                 Consumption Dataset",
  booktitle =    "2016 International Joint Conference on Neural Networks
                 (IJCNN)",
  year =         "2016",
  pages =        "2834--2841",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IJCNN.2016.7727557",
  abstract =     "The application of genetic programming (GP) to
                 streaming data analysis appears, on the face of it, to
                 be a less than obvious choice. If nothing else, the
                 (perceived) computational cost of model building under
                 GP would preclude its application to tasks with
                 non-stationary properties. Conversely, there is a rich
                 history of applying GP to various tasks associated with
                 trading agent design for currency and stock markets. In
                 this work, we investigate the utility of a
                 coevolutionary framework originally proposed for
                 trading agent design to the related streaming data task
                 of predicting individual household electric power
                 consumption. In addition, we address several
                 benchmarking issues, such as effective preprocessing of
                 stream data using a candlestick representation
                 originally developed for financial market analysis, and
                 quantification of performance using a novel area under
                 the curve style metric for streaming data. The
                 computational cost of evolving GP solutions is
                 demonstrated to be suitable for real-time operation
                 under this task and shown to provide classification
                 performance competitive with current established
                 methods for streaming data classification. Finally, we
                 note that the individual household electric power
                 consumption dataset is more flexible than the more
                 widely used electricity utility prediction dataset,
                 because it supports benchmarking at multiple temporal
                 time scales.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Alexander Loginov Malcolm Heywood Garnett Carl Wilson

Citations