Performance evaluation of outlier rules for labelling outliers in multidimensional dataset
by Kelly C. Ramos Da Silva; Helder L. Costa De Oliveira; André C.P.L.F. De Carvalho
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 19, No. 2, 2021

Abstract: The output of outlier detection algorithm applied to multidimensional dataset usually consists of scores defining the level of abnormality of each instance. However, this process per se does not identify the outlying instances. For this purpose, it is common to use an outlier rule to convert outlier scores into labels. The problem is therefore to determine an appropriate outlier rule, based on certain patterns of the scores alone. In order to deal with this problem, we studied and evaluated several traditional robust outlier rules following a pragmatic approach. The analysis of the results was facilitated by an evaluation measure developed by us. This measure was proved to be more effective than traditional measures involving only true positive and true negative rates. By using this measure, we were able to study the behaviour of different outlier rules whose performances were evaluated under varying skewness and contamination level.

Online publication date: Tue, 17-Aug-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email