Improved extension neural network and its applications
type: Item
instance of: scholarly article
title: Improved extension neural network and its applications
zbMATH Open document ID: 1407.68423
DOI: 10.1155/2014/593021
author: Yu Zhou
author: Lian Tian
author: Linfei Liu
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Extension neural network (ENN) is a new neural network that is a combination of extension theory and artificial neural network (ANN). The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data) and the borders between clusters (boundary data) are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN) has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7016806
Wikidata QID: Q59066337
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/593021
OpenAlex ID: W2029719458
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description: scientific article; zbMATH DE number 7016806