NDLI: Learning maximal generalized decision rules via discretization, generalization and rough set feature selection

Content Provider	IEEE Xplore Digital Library
Author	Xiaohua Hu Cercone, N.
Copyright Year	1997
Description	Author affiliation: Sybase Inc., Burlington, MA, USA (Xiaohua Hu)
Abstract	We present a method to mine maximal generalized decision rules from databases by integrating discretization, generalization and rough sets feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. Primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. Horizontal reduction is accomplished by merging identical tuples after the substitution of an attribute value by its higher level value in a predefined concept hierarchy for symbolic attributes or the discretization of continuous (or numeric) attributes. In the second phase, a novel context sensitive feature merit measure is used to rank the features, a subset of relevant attributes is chosen based on rough sets theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without destroying the interdependence relationships between the classes and the attributes. Rough sets based value reduction is further performed on the reduced table and all redundant condition values are dropped, finally, tuples in the reduced table are transformed into a set of maximal generalized decision rules. The experimental results on UCI data sets and an actual market database shows that our method can dramatically reduce the feature space and improve the learning accuracy.
Starting Page	548
Ending Page	556
File Size	890811
Page Count	9
File Format	PDF
ISBN	0818682035
ISSN	10823409
DOI	10.1109/TAI.1997.632302
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spatial databases Rough sets Phase measurement Data mining Computer science Merging System testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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