中文摘要：

针对已有属性选择方法较少考虑属性获取代价和属性集维数的自动确定问题,提出一种满意属性选择方法（SA SM）,将样本分类性能、属性集维数和属性提取复杂性等多种因素综合考虑。给出了属性满意度和属性集满意度定义,设计出满意度函数,导出满意属性集评价准则,详细描述了属性选择算法。对某电信公司客户流失预测的实证结果显示,SA SM获得的命中率、覆盖率、准确率和提升系数高于属性相关性选择法、一致性选择法、实例选择法和对称不确定性选择法。证实了SA SM的有效性和实用性。

英文摘要：

Most of the existing attribute selection methods did not consider the cost of attribute extraction and automatic decision of the dimension of attribute subset.In this paper,a novel approach called satisfactory attribute selection method（SASM）is proposed which considers compromisingly classification performance of attribute samples,the dimension of attribute set and the complexity of attribute extraction.Attribute satisfactory rate and attribute set satisfactory rate are defined.Several satisfactory rate functions are designed.Satisfactory attribute set evaluation criterion is given in a mathematical way.Satisfactory attribute selection algorithm is described in detail.Experimental results of customer churn prediction for a telecommunication carrier show that SASM is superior to correlation selection,consistency selection,instance-based selection and symmetric uncertainty selection in hit rate,covering rate,accuracy rate and lift coefficient.Hence,the validity and applicability of the proposed method are verified.