eCite Digital Repository

Features of higher order neural network with adaptive neurons


Xu, S, Features of higher order neural network with adaptive neurons, Proceeding of the 2nd International Conference on Software Engineering and Data Mining (SEDM2010), 23-25 June 2010, Chengdu, China, pp. 484-488. ISBN 978-1-4244-7324-3 (2010) [Refereed Conference Paper]

Restricted - Request a copy

Copyright Statement

Copyright © 2010 IEEE

Official URL:


One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks such as determining liver disorders and predicting breast cancer recurrences. A new activation function which is a combination of sine and sigmoid functions is used as the neuron activation function for the new HONN model. There are free parameters in the new activation function. 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 HONN model offers several advantages over conventional ANN models such as improved generalisation capabilities as well as abilities in handling missing values in a dataset.

Item Details

Item Type:Refereed Conference Paper
Keywords:higher order neural network, data mining, adaptive neural network, neuron activation function
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Other information and communication services
Objective Field:Other information and communication services not elsewhere classified
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:64303
Year Published:2010
Deposited By:Information and Communication Technology
Deposited On:2010-07-15
Last Modified:2014-12-22

Repository Staff Only: item control page