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Utilizing Feature Selection on Higher Order Neural Networks
chapter
posted on 2023-05-22, 16:53 authored by Zhao, Z, Shuxiang XuShuxiang Xu, Byeong KangByeong Kang, Kabir, MMJ, Liu, Y, Wasinger, RArtificial 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 RecognitionEditors
M ZhangPagination
375-390ISBN
9781522500636Department/School
School of Information and Communication TechnologyPublisher
Information Science ReferencePlace of publication
Hershey PA, USAExtent
18Rights statement
Copyright 2016 IGI GlobalRepository Status
- Restricted