期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2016
卷号：10
期号：2
页码：217-228
DOI：10.14257/ijseia.2016.10.2.18
出版社：SERSC
摘要：The motivation of this paper is based on a hypothesis that non-linear decision nodes provide a better classification performance than axis-parallel decision nodes do in many practical problems, such as image classification, and voice classification. The algorithm – MNCS_DT is introduced in this paper to create non-linear splits nodes by novel discriminant analysis in decision tree for multi-classification problem and take cost- sensitive problem into account when the features are selected. In experiment part, we use four UCI data sets to compare the performance of MNCS_DT and C4.5 CS by costs and error rates. The performance of MNCS_DT is better than C4.5 CS. And eight data sets from UCI are used to compare the performance of three different feature sets measured by accuracy, G-mean, and operation time. The performance of feature set consisting of features that follow multivariate normal distribution and altered information gain values higher than average one is better than two other feature sets in most data sets.
关键词：non-linear; cost-sensitive; multi-class; decision tree
