Visualizing Outlier Explanations for Mixed-Type Data

Jakob Nonnenmacher () and Jorge Marx Gómez
Additional contact information
Jakob Nonnenmacher: University of Oldenburg
Jorge Marx Gómez: University of Oldenburg

A chapter in Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, 2024, pp 155-163 from Springer

Abstract: Abstract Outlier explanation approaches are used to support analysts in investigating outliers, especially those detected by methods that are not intuitively interpretable such as deep learning or ensemble approaches. Of the existing studies, few consider how the obtained explanations can be visualized. Two studies exist that utilize two-dimensional scatterplots for visualizing outliers detected on numerical data. None of the existing studies explore how outlier explanations obtained for mixed-type data can be visualized. In this paper, we propose an approach for visualization that can work in tandem with recently proposed explanation approaches. For this, we use the output of the explanation method to propose multiple adaptations to parallel coordinate plots to further aid analysts in the inspection of outliers detected on mixed-type data. We evaluate our approach by conducting a focus group with potential users of the method. The focus group shows the general efficacy of the approach but also highlights avenues for further improvements.

Keywords: Outlier visualization; Outlier explanation; Outlier detection; Mixed-type data (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-56576-2_14

Ordering information: This item can be ordered from
http://www.springer.com/9783031565762

DOI: 10.1007/978-3-031-56576-2_14

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().