eCite Digital Repository

Utilizing Feature Selection on Higher Order Neural Networks


Zhao, Z and Xu, S and Kang, BH and Kabir, MMJ and Liu, Y and Wasinger, R, Utilizing Feature Selection on Higher Order Neural Networks, Applied Artificial Higher Order Neural Networks for Control and Recognition, Information Science Reference, M Zhang (ed), Hershey PA, USA, pp. 375-390. ISBN 9781522500636 (2016) [Research Book Chapter]

Copyright Statement

Copyright 2016 IGI Global

Official URL:

DOI: doi:10.4018/978-1-5225-0063-6.ch015


Artificial Neural Network has shown its impressive ability on many real world problems such as pattern recognition, classification and function approximation. An extension of ANN, higher order neural network (HONN), improves ANNís computational and learning capabilities. However, the large number of higher order attributes leads to long learning time and complex network structure. Some irrelevant higher order attributes can also hinder the performance of HONN. In this chapter, feature selection algorithms will be used to simplify HONN architecture. Comparisons of fully connected HONN with feature selected HONN demonstrate that proper feature selection can be effective on decreasing number of inputs, reducing computational time, and improving prediction accuracy of HONN.

Item Details

Item Type:Research Book Chapter
Keywords:Artificial Neural Network, Higher Order Neural Network, Feature Selection, Attribute Selection, Credit Rating, Machine Learning
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:Zhao, Z (Dr Zongyuan Zhao)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Kang, BH (Professor Byeong Kang)
UTAS Author:Kabir, MMJ (Mr Mir Kabir)
UTAS Author:Wasinger, R (Dr Rainer Wasinger)
ID Code:109090
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2016-05-20
Last Modified:2019-12-11

Repository Staff Only: item control page