NDLI: Logistic regression-based pattern classifiers for symbolic interval data

Content Provider	Springer Nature Link
Author	Souza, Renata M. C. R. Queiroz, Diego C. F. Cysneiros, Francisco José A.
Copyright Year	2011
Abstract	This paper introduces different pattern classifiers for interval data based on the logistic regression methodology. Four approaches are considered. These approaches differ according to the way of representing the intervals. The first classifier considers that each interval is represented by the centres of the intervals and performs a classic logistic regression on the centers of the intervals. The second one assumes each interval as a pair of quantitative variables and performs a conjoint classic logistic regression on these variables. The third one considers that each interval is represented by its vertices and a classic logistic regression on the vertices of the intervals is applied. The last one assumes each interval as a pair of quantitative variables, performs two separate classic logistic regressions on these variables and combines the results in some appropriate way. Experiments with synthetic data sets and an application with a real interval data set demonstrate the usefulness of these classifiers.
Starting Page	273
Ending Page	282
Page Count	10
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	14
Issue Number	3
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-06-03
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Interval symbolic data Classification Logistic regression Symbolic data analysis Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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