Publication details

Extraction of outliers from imbalanced sets



Year of publication 2017
Type Article in Proceedings
Conference Lecture Notes in Computer Science
MU Faculty or unit

Faculty of Medicine

Field Applied statistics, operation research
Keywords Outlier analysis;Distance based method;Global outlier;Single cluster;Mahalanobis distance;Biology
Description In this paper, we presented an outlier detection method, designed for small datasets, such as datasets in animal group behaviour research. The method was aimed at detection of global outliers in unlabelled datasets where inliers form one predominant cluster and the outliers are at distances from the centre of the cluster. Simultaneously, the number of inliers was much higher than the number of outliers. The extraction of exceptional observations (EEO) method was based on the Mahalanobis distance with one tuning parameter. We proposed a visualization method, which allows expert estimation of the tuning parameter value. The method was tested and evaluated on 44 datasets. Excellent results, fully comparable with other methods, were obtained on datasets satisfying the method requirements. For large datasets, the higher computational requirement of this method might be prohibitive. This drawback can be partially suppressed with an alternative distance measure. We proposed to use Euclidean distance in combination with standard deviation normalization as a reliable alternative.

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