NDLI: Quantile–DEA classifiers with interval data

Content Provider	Springer Nature Link
Author	Wei, Quanling Chang, Tsung Sheng Han, Song
Copyright Year	2014
Abstract	This research intends to develop the classifiers for dealing with binary classification problems with interval data whose difficulty to be tackled has been well recognized, regardless of the field. The proposed classifiers involve using the ideas and techniques of both quantiles and data envelopment analysis (DEA), and are thus referred to as quantile–DEA classifiers. That is, the classifiers first use the concept of quantiles to generate a desired number of exact-data sets from a training-data set comprising interval data. Then, the classifiers adopt the concept and technique of an intersection-form production possibility set in the DEA framework to construct acceptance domains with each corresponding to an exact-data set and thus a quantile. Here, an intersection-form acceptance domain is actually represented by a linear inequality system, which enables the quantile–DEA classifiers to efficiently discover the groups to which large volumes of data belong. In addition, the quantile feature enables the proposed classifiers not only to help reveal patterns, but also to tell the user the value or significance of these patterns.
Starting Page	535
Ending Page	563
Page Count	29
File Format	PDF
ISSN	02545330
Journal	Annals of Operations Research
Volume Number	217
Issue Number	1
e-ISSN	15729338
Language	English
Publisher	Springer US
Publisher Date	2014-03-02
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data envelopment analysis Classifier Quantile Production possibility set Interval data Operations Research/Decision Theory Combinatorics Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research

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