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Evolving polynomial neural networks for detecting abnormal patterns

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

Abnormal patterns, existing e.g. in raw data, affect decision making process and have to be accurately detected and removed in order to reduce the risk of making wrong decisions. Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible over-fitting. We propose a new evolutionary strategy of learning an ANN structure of a near optimal connectivity from the given data and show that such structures are less prone to over-fitting. The proposed method starts to learn with one input variable and one neuron and then adds a new input and a new neuron to the network while its validation error decreases. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 74-80

Publication milestones

  • Published - 10/11/2016

Publication status

Published - 10/11/2016

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781509013531

External Publication IDs

  • handle.net: 10547/624222
  • Scopus: 85006061967

Host publication title

2016 IEEE 8th International Conference on Intelligent Systems (IS)

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