A Binary Neural Network Framework for Attribute Selection and Prediction

Victoria J. Hodge, Tom Jackson, Jim Austin

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.

Paper Citation

in Harvard Style

J. Hodge V., Jackson T. and Austin J. (2012). A Binary Neural Network Framework for Attribute Selection and Prediction . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 510-515. DOI: 10.5220/0004150705100515

in Bibtex Style

@conference{ncta12,
author={Victoria J. Hodge and Tom Jackson and Jim Austin},
title={A Binary Neural Network Framework for Attribute Selection and Prediction},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={510-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004150705100515},
isbn={978-989-8565-33-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - A Binary Neural Network Framework for Attribute Selection and Prediction
SN - 978-989-8565-33-4
AU - J. Hodge V.
AU - Jackson T.
AU - Austin J.
PY - 2012
SP - 510
EP - 515
DO - 10.5220/0004150705100515