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.
[1] MITCHELL T M. Machine Learning. New York, USA: McGraw-Hill Science, 1997. [2] QUINLAN J R. Induction of Decision Trees. Machine Learning,1986, 1(1): 81-106. [3] WU X D, KUMAR V, QUINLAN J R, et al. Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 2008, 14(1): 1-37. [4] BREIMAN L, FRIEDMAN J H, STONE C J, et al. Classification and Regression Trees. Monterey, USA: Chapman and Hall, 1984. [5] WANG X Z, YEUNG D S, TSANG E C. A Comparative Study on Heuristic Algorithms for Generating Fuzzy Decision Trees. IEEE Trans on Systems, Man, and Cybernetics (Cybernetics), 2001, 31(2): 215-226. [6] 王熙照,洪家荣.区间值属性决策树学习算法.软件学报, 1998, 9(8): 637-640. (WANG X Z, HONG J R. Learning Algorithm of Decision Tree Generation for Interval-Valued Attributes. Journal of Software, 1998, 9(8): 637-640.) [7] HU Q H, GUO M Z, YU D R, et al. Information Entropy for Ordinal Classification. Science China(Information Sciences), 2010, 53(6): 1188-1200. [8] HU Q H, CHE X J, ZHANG L, et al. Rank Entropy-Based Decision Trees for Monotonic Classification. IEEE Trans on Knowledge and Data Engineering, 2012, 24(11): 2052-2064. [9] 陈建凯,王熙照,高相辉.改进的基于排序熵的有序决策树算法.模式识别与人工智能, 2014, 27(2): 134-140. (CHEN J K, WANG X Z, GAO X H. Improved Ordinal Decision Trees Algorithms Based on Rank Entropy. Pattern Recognition and Artificial Intelligence, 2014, 27(2): 134-140.) [10] CHEN J K, ZHAI J H, WANG X Z. Study and Improvement of Ordinal Decision Trees Based on Rank Entropy // Proc of the 13th International Conference on Machine Learning and Cybernetics. Lanzhou, China, 2014: 207-218. [11] WANG X, ZHAI J H, CHEN J K, et al. Ordinal Decision Trees Based on Fuzzy Rank Entropy // Proc of the International Conference on Wavelet Analysis and Pattern Recognition. Guangzhou, China, 2015: 208-213. [12] 刘 健,刘思峰.属性值为区间数的多属性决策对象排序研究.中国管理科学, 2010, 18(3): 90-94. (LIU J, LIU S F. Research on the Ranking of Multiple Decision Object for Attribute Value within Interval Numbers. Chinese Journal of Management Science, 2010, 18(3): 90-94.) [13] ZHAO B, WANG F, ZHANG C S. Block-Quantized Support Vector Ordinal Regression. IEEE Trans on Neural Networks, 2009, 20(5): 882-890. [14] SUN B Y, LI J Y, WU D D, et al. Kernel Discriminant Learning for Ordinal Regression. IEEE Trans on Knowledge and Data Engineering, 2010, 22(6): 906-910.
2016, Vol. 29 
