Information entropy for ordinal classification
Information entropy for ordinal classification作者机构：1. Harbin Institute of Technology Harbin 150001 China
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2010年第53卷第6期
页 面：1188-1200页
核心收录：
学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学]
基 金：supported by the National Natural Science Foundation of China (Grant Nos. 60703013, 10978011) the Key Program of the National Natural Science Foundation of China (Grant No. 60932008) the National Science Fund for Distinguished Young Scholars (Grant No. 50925625) China Postdoctoral Science Foundation
主 题：ordinal classification information entropy ranking entropy ranking mutual information
摘 要：Ordinal classification plays an important role in various decision making ***, little attention is paid to this type of learning tasks compared with general classification *** information entropy and the derived measure of mutual information play a fundamental role in a number of learning algorithms including feature evaluation, selection and decision tree *** measures are not applicable to ordinal classification for they cannot characterize the consistency of monotonicity in ordinal *** this paper, we generalize Shannon’s entropy to crisp ordinal classification and fuzzy ordinal classification, and show the information measures of ranking mutual information and fuzzy ranking mutual *** discuss the properties of these measures and show that the proposed ranking mutual information and fuzzy ranking mutual information are the indexes of consistency of monotonicity in ordinal *** addition, the proposed indexes are used to evaluate the monotonicity degree between features and decision in the context of ordinal classification.
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