Extraction of Outliers from Imbalanced Sets
Konferenční objektpeer-reviewedpostprintSoubory
Datum publikování
2017
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Název časopisu
Název svazku
Vydavatel
Springer
Abstrakt
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
Rozsah stran
p. 402-412
ISSN
0302-9743
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Zdrojový dokument
Hybrid Artificial Intelligent Systems : 12th International Conference, HAIS 2017, proceedings
Vydavatelská verze
https://link.springer.com/chapter/10.1007%2F978-3-319-59650-1_34
Přístup k e-verzi
embargoed access
Název akce
12th International Conference, HAIS 2017 (21.06.2017 - 23.06.2017, La Rioja)
ISBN
978-3-319-59649-5
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Klíčová slova
outlier analysis, distance based method, global outlier, single cluster, Mahalanobis distance, biology