Application research of improved K-means leave one out method in rejecting of abnormal samples of coal near infrared spectrum
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摘要: 针对现有留一校验法存在剔除异常样本耗时长、误判的缺陷，提出一种K均值改进留一校验法，并将其用于煤质分析中异常样本的检测与剔除。该方法首先利用K均值聚类法对样本进行聚类，得到可疑样本；然后将可疑样本作为验证集，通过留一校验法进行二次判别，剔除异常样本。实验结果表明，K均值改进留一校验法能快速、准确剔除异常样本，提高了模型的预测精度。Abstract: In view of problems of time-consuming, misjudgment of rejecting abnormal sample existed in current leave one out method, an improved K-means leave one out method was put forward for detecting and eliminating abnormal sample in coal quality analysis. Firstly, the method uses K-means clustering method to cluster samples, and gets suspicious samples; then it takes suspicious samples as a validation set, adopts leave one out method to do quadratic distinguishing, so as to eliminate abnormal samples. The experimental results show that the K-means leave one out method can eliminate abnormal samples quickly and accurately, and improves prediction accuracy of models.
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