Approach Based on Wavelet Analysis for Detecting and Amending Anomalous Samples in Data Set
Yanpo Song · Journal of Chinese Computer Systems · 2006
Appropriate data preprocessing is the precondition to perform data mining or system modeling based on data set. It is an important to eliminate or amend the anomalous samples in data set which have been polluted. When the relationship among the samples' attributes is unknown, it's difficult to detect the anomalous samples. In this paper, an approach based on wavelet analysis for detecting and amending anomalous samples is proposed, which is able to detect and amend anomalous samples accurately because it takes full advantage of wavelet analysis' character of multiple scale. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modificatory algorithm based on Newton-Cores formula is proposed. The experiments show that the approach is accurate and practical.