CAJ | 학술논문

离群数据检测是找出与正常数据不一致的数据。学生评教中由于某种原因,会出现一些评教噪声数据。针对学生评教中噪声数据的特征,提出了一个基于熵值距离的离群点检测算法,该算法通过比较每个数据点所对应的熵值和整个数据集的熵值,来判断数据点的离群程度。仿真结果表明该算法对学生评教中出现的噪声数据具有较好的过滤效果。
리군수거검측시조출여정상수거불일치적수거。학생평교중유우모충원인,회출현일사평교조성수거。침대학생평교중조성수거적특정,제출료일개기우적치거리적리군점검측산법,해산법통과비교매개수거점소대응적적치화정개수거집적적치,래판단수거점적리군정도。방진결과표명해산법대학생평교중출현적조성수거구유교호적과려효과。
Outlier detection is to identify the inconsistent data that is different to the normal data.For some reason,there will be some noise data in the student ratings of teaching evaluation.Based on the characteristics of noise data in teaching evaluation,this paper proposes an entropy distance-based outlier detection algorithm.The algorithm by comparing the entropy between each data corresponding to and the entire data set judges the degree of outlier data.Simulation results show that the algorithm appears to have a good filtering effect to noise data in student ratings of teaching effectiveness evaluation.