Outlier Detection in Test Samples using Standard Deviation and Unsupervised Training Set Selection

Document Type : Original Article

Authors

1 Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

2 Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

Abstract

Outlier detection is a technique to identify and remove significantly different data from the more correct and consistent data in a data set. Outlier data can have negative impact on classification and clustering performance; that should be identified and removed to improve the classification efficiency. Regardless of whether a classifying technique classifies an outlier correctly, the very notion of identifying a data as outlier is of great significance.   In this paper, a new approach is proposed for outlier data detection within a test data set along with unsupervised training set selection. The selected training set is used for two-step classification. After unsupervised clustering the training set, the closest cluster to a test sample is selected using the Euclidean distance measure. Then, the outlier in the test sample is identified with the concepts of standard deviation and mean value.  The results showed by evaluating the distance of each sample of the test set with the new selected data set. the accuracy of the classifiers is enhanced after detection and elimination of outlier data.

Keywords

Main Subjects

References

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International Journal of Engineering
Volume 36, Issue 1
TRANSACTIONS A: Basics
January 2023
Pages 119-129

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