CAJ | 학술논문

为处理有测量误差的数据，并且考虑到误分类代价不仅与样本相关，而且与测试代价紧密相关，提出了一种代价敏感最优误差边界选择方法。根据不同的误差边界自适应生成测试代价及误分类代价，以最小化总代价为目标，设计了基于不同误差边界的属性选择方法，进而进行最优误差边界选择。在四个UCI标准数据集上的实验分析显示，该设计方案可有效地选出最优误差边界，以保证所选属性集合具有最小的平均总代价。
위처리유측량오차적수거，병차고필도오분류대개불부여양본상관，이차여측시대개긴밀상관，제출료일충대개민감최우오차변계선택방법。근거불동적오차변계자괄응생성측시대개급오분류대개，이최소화총대개위목표，설계료기우불동오차변계적속성선택방법，진이진행최우오차변계선택。재사개UCI표준수거집상적실험분석현시，해설계방안가유효지선출최우오차변계，이보증소선속성집합구유최소적평균총대개。
This paper proposes a cost-sensitive optimal error bound selection approach to address data with measure-ment errors. The considered misclassification costs are sensitive to both examples and test costs. With this in mind, misclassification costs and test costs are adaptively computed according to error bound. In order to minimize aver-age total cost, this paper designs an algorithm for cost-sensitive optimal error bound selection. The experimental results on four UCI databases show that the designed algorithm can select the feature set with the optimal error bound. The selected feature set leads to the lowest average total cost.