@inproceedings{3f973df93bc247b1bebc9cf31f8d5697,
title = "Study and improvement of ordinal decision trees based on rank entropy",
abstract = "Decision tree is one of the most commonly used methods of machine learning, and ordinal decision tree is an important way to deal with ordinal classification problems. Through researches and analyses on ordinal decision trees based on rank entropy, the rank mutual information for every cut of each continuous- valued attribute is necessary to determine during the selection of expanded attributes for constructing decision trees based on rank entropy in ordinal classification. Then we need to compare these values of rank mutual information to get the maximum which corresponds to the expanded attribute. As the computational complexity is high, an improved algorithm which establishes a mathematical model is proposed. The improved algorithm is theoretically proved that it only traverses the unstable cut-points without computing the values of stable cut-points. Therefore, the computational efficiency of constructing decision trees is greatly improved. Experiments also confirm that the computational time of the improved algorithm can be reduced greatly.",
keywords = "Ordinal decision tree, Rank entropy, Unstable cut-point",
author = "Jiankai CHEN and Junhai ZHAI and Xizhao WANG",
note = "This research is supported by the National Natural Science Foundation of China (61170040), by the Natural Science Foundation of Hebei Province (F2012201023, F2013201110 and F2013201220), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD2010139), by the natural science foundation of Hebei University (2011-228) , by the research projects on reform of education and teaching of Hebei University (JX07-Y-27), and by Soft science research project of Hebei Province (12457662).; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",
year = "2014",
doi = "10.1007/978-3-662-45652-1_22",
language = "English",
isbn = "9783662456514",
series = "Communications in Computer and Information Science",
publisher = "Springer Berlin",
pages = "207--218",
editor = "Xizhao WANG and Qiang HE and CHAN, {Patrick P.K.} and Witold PEDRYCZ",
booktitle = "Machine Learning and Cybernetics : 13th International Conference, Proceedings",
address = "Germany",
}