NDLI: Evolutionary Support Vector Machines for Diabetes Mellitus Diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Stoean, R. Stoean, C. Preuss, M. El-Darzi, E. Dumitrescu, D.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Craiova Univ. (Stoean, R.; Stoean, C.)
Abstract	The aim of this paper is to validate the new paradigm of evolutionary support vector machines (ESVMs) for binary classification also through an application to a real-world problem, i.e. the diagnosis of diabetes mellitus. ESVMs were developed through hybridization between the strong learning paradigm of support vector machines (SVMs) and the optimization power of evolutionary computation. Hybridization is achieved at the level of solving the constrained optimization problem within the SVMs, which is a difficult task to perform in its standard manner. ESVMs have been so far applied to the binary classification of two-dimensional points. In this paper, experiments are conducted on the benchmark problem concerning diabetes of the UCI repository of machine learning data sets. Obtained results proved the correctness and promise of the new hybridized learning technique and demonstrated its ability to solve any case of binary standard classification
Starting Page	182
Ending Page	187
File Size	8183327
Page Count	6
File Format	PDF
ISBN	142440195X
DOI	10.1109/IS.2006.348414
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-04
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Diabetes Computer science Support vector machine classification Evolutionary computation Machine learning Mathematics Lagrangian functions Intelligent systems Machine intelligence support vector machines binary classification diabetes mellitus evolutionary algorithms
Content Type	Text
Resource Type	Article

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