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Data Mining Using an Adaptive HONN Model with Hyperbolic Tangent Neurons


Xu, S, Data Mining Using an Adaptive HONN Model with Hyperbolic Tangent Neurons, Knowledge Management and Acquisition for Smart Systems and Services , 20 Aug - 3 Sept 2010, Daegu, Korea, pp. 73-81. ISBN 978-3-642-15036-4 (2010) [Refereed Conference Paper]

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Springer-Verlag Berlin Heidelberg 2010

DOI: doi:10.1007/978-3-642-15037-1_7


An Artificial Neural Network (ANN) works by creating connections between different processing elements (artificial neurons). ANNs have been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks such as determining breast cancer recurrences and predicting incomes base on census data. An adaptive hyperbolic tangent function is used as the neuron activation function for the new adaptive HONN model. The paper compares the new HONN model against a Multi-Layer Perceptron (MLP) with the sigmoid activation function, an RBF Neural Network with the Gaussian activation function, and a Recurrent Neural Network (RNN) with the sigmoid activation function. Experimental results show that the new adaptive HONN model offers several advantages over conventional ANN models such as better generalisation capabilities as well as abilities in handling missing values in a dataset.

Item Details

Item Type:Refereed Conference Paper
Keywords: neural network, data mining, higher order neural network, adaptive activation function, hyperbolic tangent activation function
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:64967
Year Published:2010
Deposited By:Information and Communication Technology
Deposited On:2010-09-16
Last Modified:2015-02-24

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