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On making sense of neural networks in road analysis

Cheong Took, C, Morris, D and Antoniades, A (2017) On making sense of neural networks in road analysis In: International Joint Conference on Neural Networks, 2017-05-14 - 2017-05-19, Anchorage, Alaska USA.

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Abstract

Neural networks have been treated as “black boxes” for the majority of the machine learning community. The difficulty in making sense of neural networks lies in the complex topology of the hidden layers. Although there have been works in the literature aimed at demystifying the way neural networks operate, making sense of the hidden layer still remains a challenge. In this work, we propose a way to derive physical meaning from the hidden layer by mapping our neural network to the topology of a Bayesian network. Using this mapping, we enhance the probabilities of the Bayesian network resulting in a hybrid model that outperforms both the Bayesian and neural networks in the task of traffic accident prediction. Our analysis suggests that a neural network can estimate the node probabilities of a Bayesian network if mapped accordingly.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Cheong Took, CUNSPECIFIEDUNSPECIFIED
Morris, DUNSPECIFIEDUNSPECIFIED
Antoniades, AUNSPECIFIEDUNSPECIFIED
Date : 14 May 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 04 Apr 2017 16:34
Last Modified : 04 Apr 2017 16:34
URI: http://epubs.surrey.ac.uk/id/eprint/813762

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