文献类型：期刊文献

英文题名：Random forest for label ranking

作者：Zhou, Yangming[1];Qiu, Guoping[2,3]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China;[3]Univ Nottingham, Sch Comp Sci, Nottingham NG7 2RD, England

年份：2018

卷号：112

起止页码：99

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20182605371734);WOS:【SCI-EXPANDED(收录号:WOS:000442708600008)】；

基金：This work was partially supported by National Natural Science Foundation of China (No.61772201).

语种：英文

外文关键词：Preference learning; Label ranking; Random forest; Decision tree

摘要：Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information. (C) 2018 Elsevier Ltd. All rights reserved.