基于决策倾向度的样本过滤与主动选择

该文提出了基于过滤函数的粗糙集样本决策倾向度动态调节与主动选择方法。首先定义样本过滤函数，从而确定样本选择或丢弃的依据；然后依次添加新增样本，根据过滤函数决定样本的去留，同时根据阈值指标调节已有样本的决策倾向度；最终建立有效的决策样本库，并在此基础上进行属性约简。本方法克服了传统变精度方法实现过程复杂、计算量大的问题，可有效地去除噪声数据，提高系统的鲁棒性。数据实验结果表明，该方法可以有效地压缩数据，提高样本分析质量。

Active Sample Selection Method Based on Decision Making Tendency

A dynamic adjustment and active selection method for rough set decision making based on filtering function is proposed. Firstly, a sample filtering function is defined to determine the basis for sample selection or discarding; then, new samples are added in turn to determine the retention of samples according to the filtering function, and the decision-making tendency of existing samples is adjusted according to the threshold; finally, new sample library is established and attribute reduction is carried out. This method overcomes the problems of complex implementation process and large amount of calculation in traditional variable precision methods, and can effectively remove noise data and improve the robustness of the system. Experimental results show that this method can effectively compress data and improve the quality of sample analysis.