Interval-Valued Attributes Based Monotonic Decision Tree Algorithm
CHEN Jiankai1, WANG Xin1, He Qiang1, WANG Xizhao2
1.Hebei Province Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding 071002 2.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060
Abstract Some learning algorithms of interval-valued attributes are developed in the disorderly situation. The ordinal relation between condition attributes and decision attributes is not taken into account. In this paper, aiming at the defects of the original algorithms, a monotonic decision tree algorithm is proposed to deal with monotonic classification of interval-valued attributes. The possibility degree is used to determine the order relation of interval-valued attributes, the rank mutual information is utilized to measure the monotonic consistency, and the expanded attributes are selected by maximizing the rank mutual information. Furthermore, unstable cut-points are applied to the construction process of interval-valued attributes decision tree to reduce the computing number of rank mutual information and improve the computational efficiency. The experimental results show that the algorithm improves the efficiency and testing accuracy.
About author:: (CHEN Jiankai， born in 1986， master， assistant economist. His research interests include machine learning.)(WANG Xin(Corresponding author)， born in 1986， master， assistant experimentalist. Her research interests include machine learning.)(He Qiang， born in 1977， Ph.D.， associate professor. His research interests include fuzzy mathematics and machine learning.)(WANG Xizhao， born in 1963， Ph.D.， professor. His research interests include machine learning and pattern recognition.)
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2016, Vol. 29 
