Extraction of outliers from imbalanced sets

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081766%3A_____%2F17%3A00477864" target="_blank" >RIV/68081766:_____/17:00477864 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/17:00098161 RIV/00216275:25530/17:39910984
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-59650-1_34" target="_blank" >http://dx.doi.org/10.1007/978-3-319-59650-1_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-59650-1_34" target="_blank" >10.1007/978-3-319-59650-1_34</a>

Result language
angličtina
Original language name
Extraction of outliers from imbalanced sets
Original language 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.
Czech name
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Czech description
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Project
<a href="/en/project/GA17-20286S" target="_blank" >GA17-20286S: Physiology of bat hibernation with respect to multistressor impacts</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Hybrid Artificial Intelligent Systems: 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017, Proceedings
ISBN
978-3-319-59649-5
ISSN
0302-9743
e-ISSN
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Number of pages
11
Pages from-to
402-412
Publisher name
Springer
Place of publication
Cham
Event location
La Rioja
Event date
Jun 21, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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