基于代价敏感的粗糙集近似集与粒度寻优算法

Approximation sets of rough sets and granularity optimization algorithm based on cost-sensitive

粗糙集的近似集用已有知识粒对不确定性目标概念进行近似描述,但在构建近似集时并没有考虑数据的代价信息这一实际因素.对此,首先分析在构建粗糙集的近似集时考虑代价信息的必要性;然后,从代价敏感角度构建误分类代价的粗糙集近似集模型,并分析该模型下求得的近似集的相关性质.为了在多粒度空间中寻找一个合适的粒度空间来对不确定性目标概念进行近似描述,使误分类代价与测试代价之和尽可能小,给出属性代价贡献率的定义,并提出一种代价敏感的粒度寻优算法.实验结果表明,所提出算法能适用于现有代价认知场景,并在给定代价场景下求出合理的层次粒度空间结构以及不确定性目标概念的近似集.

Approximation sets of rough sets use existing knowledge granules to describe the uncertain concept approximately. However, the cost information contained in data have not been considered when constructing the approximation set of the uncertain concept. Therefore, the necessity of considering cost information is analyzed when constructing the approximation sets of rough sets firstly. Then an approximation set model of rough sets of misclassification cost is constructed from the perspective of cost-sensitive, and some properties of this model are discussed in detail. In order to find a suitable granularity space to describe the uncertain concept approximately that can minimize the sum of misclassification cost and test cost as far as possible in multi-granulation spaces, the contribute rate of attribute cost is defined, and the cost-sensitive granularity optimization algorithm is proposed. The experimental results show that the proposed algorithm can be used in existing cost cognition scene. And a reasonable hierarchy granulation space and the approximation set of the uncertain concept can be obtained under the given cost scene.