Hodge, Victoria Jane orcid.org/0000-0002-2469-0224, Jackson, Tom and Austin, Jim orcid.org/0000-0001-5762-8614 (2012) A Binary Neural Network Framework for Attribute Selection and Prediction. In: Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), 05-07 Oct 2012.
Abstract
In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus journey times from traffic sensor data and show how attribute selection can both speed our k-NN and increase the prediction accuracy by removing noise and redundant attributes from the data.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Keywords: | Attribute Selection,Feature Selection,Binary Neural Network,Prediction,k-Nearest Neighbour |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 16 Jun 2016 13:39 |
Last Modified: | 12 Jan 2025 00:13 |
Published Version: | https://doi.org/10.5220/0004150705100515 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.5220/0004150705100515 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89483 |
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Filename: HodgeJacksonAustin_NCTA.pdf
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