A comparison study of optimal scale combination selection in generalized multi-scale decision tables
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要Traditional rough set approach is mainly used to unravel rules from a decision table in which objects can possess a unique attribute-value. In a real world data set, for the same attribute objects are usually measured at different scales. The main objective of this paper is to study optimal scale combinations in generalized multi-scale decision tables. A generalized multi-scale information table is an attribute-value system in which different attributes are measured at different levels of scales. With the aim of investigating knowledge representation and knowledge acquisition in inconsistent generalized multi-scale decision tables, we first introduce the notion of scale combinations in a generalized multi-scale information table. We then formulate information granules with different scale combinations in multi-scale information systems and discuss their relationships. Furthermore, we define lower and upper approximations of sets with different scale combinations and examine their properties. Finally, we examine optimal scale combinations in inconsistent generalized multi-scale decision tables. We clarify relationships among different concepts of optimal scale combinations in inconsistent generalized multi-scale decision tables.
出版社接受日期23.04.2019
著者Weizhi Wu, Yee Leung
期刊名稱International Journal of Machine Learning and Cybernetics
出版年份2020
月份5
卷號11
期次5
出版社Springer
頁次961 - 972
國際標準期刊號1868-8071
語言美式英語

上次更新時間 2020-17-09 於 00:33