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Author Chao Lu ♦ Xue- Wei Li ♦ Hong-Bo Pan
Sponsorship IEEE ♦ ICIC Int. ♦ National Natural Sci. Found. of China ♦ Beijing Jiaotong Univ. ♦ Kaosiung Univ. of Appl. Sci
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Chaos ♦ Fuzzy control ♦ Upper bound ♦ Neural networks ♦ Decision making ♦ Fuzzy neural networks ♦ Fuzzy set theory ♦ Data mining ♦ Pattern matching ♦ Testing
Abstract Classification is an important theme in data mining, but classification with incomplete survey data is a new subject. Standard neural networks and other techniques reported in the literature do not address the problem of incomplete survey data. So, this paper proposes a novel extension neural network based model of classification for incomplete survey data. The extension neural network is a combination of extension theory and neural network. It uses an extension distance to measure the similarity between data and cluster center. And also the classifier retains information of class membership for each exemplar pattern. In a real world example, the extension neural network would find an exemplar that best matches the test pattern and give the classification result. Compared with other classification techniques, the extension neural network can utilize more information provided by the data with missing values, and reveal the risk of the classification result on the individual observation basis
Description Author affiliation: Sch. of Econ. & Manage., Beijing Jiao Tong Univ. (Chao Lu; Xue- Wei Li)
ISBN 0769526160
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-08-30
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 118.25 kB
Page Count 4
Starting Page 190
Ending Page 193

Source: IEEE Xplore Digital Library