Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26569
Title: Rank entropy-based decision trees for monotonic classification
Authors: Hu, Q
Che, X
Zhang, L 
Zhang, D 
Guo, M
Yu, D
Keywords: Decision tree
Monotonic classification
Rank entropy
Rank mutual information
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE Transactions on knowledge and data engineering, 2012, v. 24, no. 11, 5936071, p. 2052-2064 How to cite?
Journal: IEEE transactions on knowledge and data engineering 
Abstract: In many decision making tasks, values of features and decision are ordinal. Moreover, there is a monotonic constraint that the objects with better feature values should not be assigned to a worse decision class. Such problems are called ordinal classification with monotonicity constraint. Some learning algorithms have been developed to handle this kind of tasks in recent years. However, experiments show that these algorithms are sensitive to noisy samples and do not work well in real-world applications. In this work, we introduce a new measure of feature quality, called rank mutual information (RMI), which combines the advantage of robustness of Shannon's entropy with the ability of dominance rough sets in extracting ordinal structures from monotonic data sets. Then, we design a decision tree algorithm (REMT) based on rank mutual information. The theoretic and experimental analysis shows that the proposed algorithm can get monotonically consistent decision trees, if training samples are monotonically consistent. Its performance is still good when data are contaminated with noise.
URI: http://hdl.handle.net/10397/26569
ISSN: 1041-4347
EISSN: 1558-2191
DOI: 10.1109/TKDE.2011.149
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