摘要
In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.
In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree（REOT） construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.
出处
《浙江海洋学院学报（自然科学版）》
CAS
2010年第5期450-461,共12页
Journal of Zhejiang Ocean University(Natural Science Edition)
基金
supported by National Natural Science Foundation of China under Grant 60703013 and 10978011
Key Program of National Natural Science Foundation of China under Grant 60932008
National Science Fund for Distinguished Young Scholars under Grant 50925625
China Postdoctoral Science Foundation.
