A Binary Neural Network Framework for Attribute Selection and Prediction

Research output: Contribution to conferencePaperpeer-review

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A Binary Neural Network Framework for Attribute Selection and Prediction. / Hodge, Victoria Jane; Jackson, Tom; Austin, Jim.

2012. 510-515 Paper presented at Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), Barcelona, Spain.

Research output: Contribution to conferencePaperpeer-review

Harvard

Hodge, VJ, Jackson, T & Austin, J 2012, 'A Binary Neural Network Framework for Attribute Selection and Prediction', Paper presented at Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), Barcelona, Spain, 5/10/12 - 7/10/12 pp. 510-515. https://doi.org/10.5220/0004150705100515

APA

Hodge, V. J., Jackson, T., & Austin, J. (2012). A Binary Neural Network Framework for Attribute Selection and Prediction. 510-515. Paper presented at Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), Barcelona, Spain. https://doi.org/10.5220/0004150705100515

Vancouver

Hodge VJ, Jackson T, Austin J. A Binary Neural Network Framework for Attribute Selection and Prediction. 2012. Paper presented at Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), Barcelona, Spain. https://doi.org/10.5220/0004150705100515

Author

Hodge, Victoria Jane ; Jackson, Tom ; Austin, Jim. / A Binary Neural Network Framework for Attribute Selection and Prediction. Paper presented at Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), Barcelona, Spain.5 p.

Bibtex - Download

@conference{e4a71cd09de04e83925586724a7113fa,
title = "A Binary Neural Network Framework for Attribute Selection and Prediction",
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.",
keywords = "Attribute Selection, Feature Selection, Binary Neural Network, Prediction, k-Nearest Neighbour",
author = "Hodge, {Victoria Jane} and Tom Jackson and Jim Austin",
year = "2012",
month = oct,
day = "5",
doi = "10.5220/0004150705100515",
language = "English",
pages = "510--515",
note = "Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012) ; Conference date: 05-10-2012 Through 07-10-2012",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - A Binary Neural Network Framework for Attribute Selection and Prediction

AU - Hodge, Victoria Jane

AU - Jackson, Tom

AU - Austin, Jim

PY - 2012/10/5

Y1 - 2012/10/5

N2 - 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.

AB - 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.

KW - Attribute Selection

KW - Feature Selection

KW - Binary Neural Network

KW - Prediction

KW - k-Nearest Neighbour

U2 - 10.5220/0004150705100515

DO - 10.5220/0004150705100515

M3 - Paper

SP - 510

EP - 515

T2 - Proceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012)

Y2 - 5 October 2012 through 7 October 2012

ER -