Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?

This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Rand...

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Bibliographic Details
Published inAdvances in Intelligent Data Analysis XIV Vol. 9385; pp. 193 - 204
Main Authors Nezvalová, Leona, Popelínský, Luboš, Torgo, Luis, Vaculík, Karel
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Class Outlier Detection
Exceptional Model Mining
Image processing
Outer Class
Outlier Factor
Outlier Interpretation
Pattern recognition
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Summary:This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation.
ISBN:3319244647
9783319244648
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-24465-5_17