摘要
属性与尺度同步选择方法可有效解决涉及代价因素的多尺度决策系统的知识约简问题,然而在现有研究中,少有基于代价进行属性与尺度的同步选择,并且大多数算法只针对协调的多尺度决策系统或不协调的多尺度决策系统.为了解决这一问题,文中以最小化数据处理的总测试代价为目标,提出测试代价敏感的属性与尺度同步选择算法,同时适用于协调的多尺度决策系统和不协调的多尺度决策系统.首先,构造基于粗糙集的理论模型,模型中的概念及性质同时考虑属性因素和尺度因素.其次,基于粗糙集的理论模型,设计启发式算法,能基于测试代价对多尺度决策系统同时进行属性约简与尺度选择,并且不同的属性可选择不同的尺度.最后,在12个数据集上的实验验证文中算法的有效性、实用性及优越性.
Multi-scale decision system is one of hot issues in the field of data mining,and cost factors appear frequently in data mining.A method for simultaneous selection of attributes and scales can effectively solve the knowledge reduction problem of multi-scale decision systems involving cost factors.However,in the existing research,there are few studies on the simultaneous selection of attributes and scales based on costs,and most of the algorithms only focus on consistent or inconsistent multi-scale decision systems.To address this issue,a test cost sensitive method for simultaneously selecting attributes and scales is proposed with the goal of minimizing the total test cost of data processing.The method is applicable to both consistent and inconsistent multi-scale decision systems.Firstly,a theoretical model is constructed based on rough set.In the model,both the attribute factor and the scale factor are taken into Recommended by Associate Editor ZHANG Yanping account by concepts and properties.Secondly,a heuristic algorithm is designed based on the theoretical model.By the proposed algorithm,attribute reduction and scale selection can be simultaneously performed in the multi-scale decision systems based on test costs,and different attributes can choose different scales.Finally,the experiments verify the effectiveness,practicality and superiority of the proposed algorithm.
作者
廖淑娇
吴迪
卢亚倩
范译文
LIAO Shujiao;WU Di;LU Yaqian;FAN Yiwen(School of Mathematics and Statistics,Minnan Normal University,Zhangzhou 363000;Fujian Key Laboratory of Granular Computing and Applications,Minnan Normal University,Zhangzhou 363000;Institute of Meteorological Big Data-Digital Fujian,Minnan Normal University,Zhangzhou 363000;Fujian Key Laboratory of Data Science and Statistics,Minnan Normal University,Zhangzhou 363000)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2024年第4期368-382,共15页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.12101289)资助。
关键词
属性与尺度选择
代价敏感学习
多尺度决策系统
粗糙集
单调性
Attribute and Scale Selection
Cost-Sensitive Learning
Multi-scale Decision System
Rough Set
Monotonicity
