Volume 23, Issue 4 (IJIEPM 2013)                   2013, 23(4): 485-501 | Back to browse issues page

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Kazemi A, Aboutaleb S. Presenting a multi-objective mathematical optimization model for classification in data mining. Journal title 2013; 23 (4) :485-501
URL: http://ijiepm.iust.ac.ir/article-1-536-en.html
Islamic Azad university , abkaazemi@qiau.ac.ir
Abstract:   (11551 Views)
In this paper we investigate the issues of data classification (as one of the branches of data mining science) in form of multi-objective mathematical programming model. The model that we present and investigate is a MODM problem. First time, based on support vector machine (SVM) idea (To maximize the margin of two groups), a multi-criteria mathematical programming model was proposed for data mining problems to classified instances in two separated group based on two goals of data discriminate (To maximize the distance between different groups and to minimize the misclassification of observations). Since then, some people have been working on developing the classification models based on mathematical programming methods. Simultaneously and also independently, individuals worked on support vector machine methods to improve them. Regarding to the same philosophy of these two groups of optimization methods, in this paper, to fill the gap between these two research paths, we use the updated and improved SVM methods to present a model for classification in data mining based on multi-objective programming.
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Type of Study: Research | Subject: Optimization Techniques
Received: 2011/04/6 | Published: 2013/02/15

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