NDLI: An evolution strategies approach to the simultaneous discretization of numeric attributes in data mining

Content Provider	IEEE Xplore Digital Library
Author	Valdes, J.J. Molina, L.C. Peris, N.
Copyright Year	2003
Description	Author affiliation: Inst. for Inf. Technol., Nat. Res. Council of Canada, Ottawa, Ont., Canada (Valdes, J.J.)
Abstract	Many data mining and machine learning algorithms require databases in which objects are described by discrete attributes. However, it is very common that the attributes are in the ratio or interval scales. In order to apply these algorithms, the original attributes must be transformed into the nominal or ordinal scale via discretization. An appropriate transformation is crucial because of the large influence on the results obtained from data mining procedures. This paper presents a hybrid technique for the simultaneous supervised discretization of continuous attributes, based on evolutionary algorithms, in particular, evolution strategies (ES), which is combined with rough set theory and information theory. The purpose is to construct a discretization scheme for all continuous attributes simultaneously (i.e. global) in such a way that class predictability is maximized w.r.t the discrete classes generated for the predictor variables. The ES approach is applied to 17 public data sets and the results are compared with classical discretization methods. ES-based discretization not only outperforms these methods, but leads to much simpler data models and is able to discover irrelevant attributes. These features are not present in classical discretization techniques.
Sponsorship	Air Force Iffice of Sci. Res. Asian Office of Aerospace Res and Development Army Res. Office - Far East CISCO Evoluationary Programming Soc. IEE Inst. of Engineers, Australia IEEE Neural Networks Soc. Univ. of New South Wales
Starting Page	1957
Ending Page	1964
File Size	590761
Page Count	8
File Format	PDF
ISBN	0780378040
DOI	10.1109/CEC.2003.1299913
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Mexico Council Machine learning algorithms Set theory Petroleum Cities and towns Information technology Machine learning Databases Data models
Content Type	Text
Resource Type	Article

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