PERFORMANCE OF MACHINE LEARNING METHODS IN CLASSIFICATION MODELS WITH HIGH-DIMENSIONAL DATA

被引：0

作者：

Zekic-Susac, Marijana ^{[1
]}

Pfeifer, Sanja ^{[1
]}

Sarlija, Natasa ^{[1
]}

机构：

[1] Univ Josip Juraj Strossmayer Osijek, Fac Econ, Gajev Trg 7, Osijek 31000, Croatia

来源：

SOR'13 PROCEEDINGS: THE 12TH INTERNATIONAL SYMPOSIUM ON OPERATIONAL RESEARCH IN SLOVENIA | 2013年

关键词：

machine learning; support vector machines; artificial neural networks; CART classification trees; k-nearest neighbour; large-dimensional data; cross-validation; SUPPORT VECTOR MACHINES; NEURAL-NETWORKS; DECISION TREES;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

The paper investigates the performance of four machine learning methods: artificial neural networks, classification trees, support vector machines, and k-nearest neighbour in classification type of problem by using a real dataset on entrepreneurial intentions of students. The aim is to fmd out which of the machine learning methods is more efficient in modelling high-dimensional data in the sense of the average classification rate obtained in a 10-fold cross-validation procedure. In addition, sensitivity and specificity is also observed. The results show that the accuracy of artificial neural networks is significantly higher than the accuracy of k-nearest neighbour, but the difference among other methods is not statistically significant.

引用

页码：219 / 224

页数：6

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