Vladimir Kascelan

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Popular decision tree (DT) algorithms such as ID3, C4.5, CART, CHAID and QUEST may have different results using same data set. They consist of components which have similar functionalities. These components implemented on different ways and they have different performance. The best way to get an optimal DT for a data set is one that use component-based(More)
Environmental degradation by greenhouse gas (GHG) emissions has been an important challenge of sustainable economic development and climate changes control. Industry is the major source of CO2 emissions, whereas 84% of global anthropogenic methane and nitrous-oxide emissions emerge from agriculture. The impact of agro-economic factors on GHG emissions in(More)
Analysis of investors’ preferences in the Montenegro stock market using data mining techniques Ljiljana Kašćelan, Vladimir Kašćelan & Miomir Jovanović To cite this article: Ljiljana Kašćelan, Vladimir Kašćelan & Miomir Jovanović (2014) Analysis of investors’ preferences in the Montenegro stock market using data mining techniques, Economic Research-Ekonomska(More)
This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as(More)
For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that(More)
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