协调多尺度决策系统中基于测试代价的属性与尺度选择

Attribute and Scale Selection Based on Test Cost in Consistent Multi-scale Decision Systems

对多尺度决策系统进行处理可以使复杂的问题简单化,属性与尺度的同步选择是该处理过程中一个重要方法.此外,现实中数据处理经常需要考虑代价因素的影响,但是,目前研究还没有在属性与尺度的同步选择中考虑代价因素.为了解决这一问题,文中基于测试代价,研究协调多尺度决策系统的属性与尺度选择.首先,构造相应的粗糙集理论模型,模型中的定义及性质同时考虑属性和尺度这两个要素,并给出基于测试代价的属性-尺度重要度函数.然后,基于适用于多尺度决策系统的粗糙集概念及性质,提出属性与尺度同步选择的启发式算法.在UCI数据集上的实验表明,文中算法可大幅降低总测试代价,提升计算效率.

The processing of multi-scale decision systems can simplify the complex problem,and simultaneous selection of attributes and scales is an important method in this process.In addition,the influence of cost factors is often taken into consideration in practical data processing.However,there is no research on cost factors in the simultaneous selection of attributes and scales.To solve this problem,the method of attribute and scale selection based on test cost in consistent multi-scale decision systems is proposed in this paper.Firstly,a corresponding rough set theoretical model is constructed.Both attribute and scale are considered in definitions and properties of the constructed theoretical model,and the test cost-based attribute-scale significance function is provided.Then,on the basis of concepts and properties of rough set applicable to multi-scale decision systems,a heuristic algorithm for simultaneous selection of attributes and scales is proposed.Experiments on UCI dataset show that the proposed algorithm significantly reduces the total test cost and improves computational efficiency.