| 一种新的排序学习算法 |
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| 引用本文： | 刘华富,潘怡,王仲.一种新的排序学习算法[J].东南大学学报,2007,23(3):447-450. |
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| 作者姓名： | 刘华富 潘怡 王仲 |
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| 作者单位： | 长沙大学计算机科学与技术系 长沙410003 |
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| 基金项目： | The Planning Program of Science and Technology of Hunan Province (No05JT1039) |
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| 摘 要： | 为了克服排序学习算法不能处理包括名词性特征的复杂数据类型的局限性,设计一种新的排序学习算法.在决策树学习算法中,采用新的等级不纯度定义,修改决策树的分裂规则,得到具有直观解释的排序算法,并给出了相关理论基础.实验结果表明:排序树的平均等级损失明显优于感知机类算法和序回归类算法,且具有较快的收敛速度.基于决策树的排序学习算法,可以处理名词性数据和选择相关的特征.
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| 关 键 词： | 机器学习 排序学习算法 决策树 分裂规则 |
| 修稿时间： | 2007-05-18 |
New rank learning algorithm |
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| Liu Huafu,Pan Yi,Wang Zhong.New rank learning algorithm[J].Journal of Southeast University(English Edition),2007,23(3):447-450. |
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| Authors: | Liu Huafu Pan Yi Wang Zhong |
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| Institution: | Department of Computer Science and Technology, Changsha University, Changsha 410003, China |
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| Abstract: | To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms,a new rank learning algorithm is designed.In the learning algorithm based on the decision tree,the splitting rule of the decision tree is revised with a new definition of rank impurity.A new rank learning algorithm,which can be intuitively explained,is obtained and its theoretical basis is provided.The experimental results show that in the aspect of average rank loss,the ranking tree algorithm outperforms perception ranking and ordinal regression algorithms and it also has a faster convergence speed.The rank learning algorithm based on the decision tree is able to process categorical data and select relative features. |
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| Keywords: | machine learning rank learning algorithm decision tree splitting rule |
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