Test Cost Sensitive Simultaneous Selection of Attributes and Scales in Multi-scale Decision Systems
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 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.
Attribute and Scale SelectionCost-Sensitive LearningMulti-scale Decision SystemRough SetMonotonicity
万方数据
维普
