Evaluating difficulty of multi-class imbalanced data

Mateusz Lango; Krystyna Napierała; Jerzy Stefanowski

doi:10.1007/978-3-319-60438-1_31

Scientific Information System of the Poznań University of Technology

PL EN

Main page / Publications / Evaluating difficulty of multi-class imbalanced data

Submit a comment

Chapter

Download BibTeX

Title

Evaluating difficulty of multi-class imbalanced data

Authors

Mateusz Lango (WI) ^{[ 1 ][ 2.3 ][ P ]}
Krystyna Napierała (WI) ^{[ 1 ]}
Jerzy Stefanowski (WI) ^{[ 1 ][ 2.3 ][ P ]}

^{[ 1 ]} Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2017

Chapter type

chapter in monograph / paper

Publication language

english

Keywords

EN

imbalanced data
multiple classes
supervised classification

Abstract

EN Multi-class imbalanced classification is more difficult than its binary counterpart. Besides typical data difficulty factors, one should also consider the complexity of relations among classes. This paper introduces a new method for examining the characteristics of multi-class data. It is based on analyzing the neighbourhood of the minority class examples and on additional information about similarities between classes. The experimental study has shown that this method is able to identify the difficulty of class distribution and that the estimated minority example safe levels are related with prediction errors of standard classifiers.

Date of online publication

04.06.2017

Pages (from - to)

312 - 322

DOI

10.1007/978-3-319-60438-1_31

URL

https://link.springer.com/chapter/10.1007/978-3-319-60438-1_31

Book

Foundations of Intelligent Systems : 23rd International Symposium, ISMIS 2017, Warsaw, Poland, June 26-29, 2017 : Proceedings

Presented on

23rd International Symposium on Methodologies for Intelligent Systems ISMIS 2017, 26-29.06.2017, Warsaw, Poland