A Binary Neural Network Framework for Attribute Selection and Prediction

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ConferenceProceedings of the 4th International Conference on Neural Computation Theory and Applications (NCTA 2012)
Conference date(s)5/10/127/10/12

Publication details

DatePublished - 5 Oct 2012
Number of pages5
Original languageEnglish


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.

    Research areas

  • Attribute Selection, Feature Selection, Binary Neural Network, Prediction, k-Nearest Neighbour

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