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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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