NDLI: Interval-based attribute evaluation algorithm

Content Provider	IEEE Xplore Digital Library
Author	Salama, M.A. El-Bendary, N. Hassanien, A.E. Revett, K. Fahmy, A.A.
Copyright Year	2011
Description	Author affiliation: Arab Academy for Science, Technology, and Maritime Transport, Cairo, Egypt (El-Bendary, N.) \|\| Cairo University, Faculty of Computers and Information, Cairo, Egypt (Hassanien, A.E.; Fahmy, A.A.) \|\| Department of Computer Science, British University in Egypt, Cairo, Egypt (Salama, M.A.; Revett, K.)
Abstract	Attribute values may be either discrete or continuous. Attribute selection methods for continuous attributes had to be preceded by a discretization method to act properly. The resulted accuracy or correctness has a great dependance on the discretization method. However, this paper proposes an attribute selection and ranking method without introducing such technique. The proposed algorithm depends on a hypothesis that the decrease of the overlapped interval of values for every class label indicates the increase of the importance of such attribute. Such hypothesis were proved by comparing the results of the proposed algorithm to other attribute selection algorithms. The comparison between different attribute selection algorithms is based on the characteristics of relevant and irrelevant attributes and their effect on the classification performance. The results shows that the proposed attribute selection algorithm leads to a better classification performance than other methods. The test is applied on medical data sets that represent a real life continuous data sets.
Starting Page	153
Ending Page	156
File Size	134618
Page Count	4
File Format	PDF
ISBN	9781457700415
e-ISBN	9788360810392
e-ISBN	9788360810354
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-18
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Polish Info Processing Soc
Subject Keyword	Support vector machines Training Computer science Accuracy Conferences ChiMerge Classification Attribute selection Classification algorithms Testing
Content Type	Text
Resource Type	Article

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