Benchmarking a Coevolutionary Streaming Classifier under the Individual Household Electric Power Consumption Dataset
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gp-bibliography.bib Revision:1.7954
- @InProceedings{Loginov:2016:IJCNN,
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author = "Alexander Loginov and Malcolm I. Heywood and
Garnett Wilson",
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title = "Benchmarking a Coevolutionary Streaming Classifier
under the Individual Household Electric Power
Consumption Dataset",
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booktitle = "2016 International Joint Conference on Neural Networks
(IJCNN)",
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year = "2016",
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pages = "2834--2841",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/IJCNN.2016.7727557",
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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.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Alexander Loginov
Malcolm Heywood
Garnett Carl Wilson
Citations