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Utilizing Feature Selection on Higher Order Neural Networks

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posted on 2023-05-22, 16:53 authored by Zhao, Z, Shuxiang XuShuxiang Xu, Byeong KangByeong Kang, Kabir, MMJ, Liu, Y, Wasinger, R
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 ANN’s 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.

History

Publication title

Applied Artificial Higher Order Neural Networks for Control and Recognition

Editors

M Zhang

Pagination

375-390

ISBN

9781522500636

Department/School

School of Information and Communication Technology

Publisher

Information Science Reference

Place of publication

Hershey PA, USA

Extent

18

Rights statement

Copyright 2016 IGI Global

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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