NDLI: Uni-class pattern-based classification model

Content Provider	IEEE Xplore Digital Library
Author	Salama, M.A. Hassanien, A.E. Fahmy, A.A.
Copyright Year	2010
Description	Author affiliation: Faculty of Computers and Information, British University in Egypt, Cairo, Egypt (Salama, M.A.) \|\| Faculty of Computers and Information, Cairo University, Cairo, Egypt (Hassanien, A.E.; Fahmy, A.A.)
Abstract	This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence problem in this clustering technique has been resolved in the proposed model. Also this model solve the curse of dimensionality problem without the need of using a separate dimensionality reduction method. Another distinguishing point in this model is its dependence on the variation of the values of relative features among different objects. Experimental results on synthetic and real life datasets show that this approach is more efficient and effective than the existing techniques.
Starting Page	1293
Ending Page	1297
File Size	191564
Page Count	5
File Format	PDF
ISBN	9781424481347
e-ISBN	9781424481361
DOI	10.1109/ISDA.2010.5687087
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-29
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Feature extraction Data models Data mining Intelligent systems Testing Diseases
Content Type	Text
Resource Type	Article

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