Arithmetic Means of Accuracies: A Classifier Performance Measurement for Imbalanced Data Set

Beitrag bei einer Tagung
(Originalarbeit)


Details zur Publikation

Autor(en): Timotius I, Miaou SG
Verlag: IEEE
Jahr der Veröffentlichung: 2010
Tagungsband: International Conference on Audio, Language and Image Processing (ICALIP 2010)
Seitenbereich: 1244 - 1251
ISBN: 978-1-4244-5856-1
Sprache: Englisch


Abstract


Classifier performance measurement is essential in the development and analysis of classification algorithms. This paper proposes a new measurement approach that can be used generally for the balanced and imbalanced data set, can reflect the random guessing behavior perfectly, and can be used easily in cost-sensitive classification and multiple-class classification.



FAU-Autoren / FAU-Herausgeber

Timotius, Ivanna
Lehrstuhl für Informatik 5 (Mustererkennung)


Zitierweisen

APA:
Timotius, I., & Miaou, S.-G. (2010). Arithmetic Means of Accuracies: A Classifier Performance Measurement for Imbalanced Data Set. In International Conference on Audio, Language and Image Processing (ICALIP 2010) (pp. 1244 - 1251). Shanghai, CN: IEEE.

MLA:
Timotius, Ivanna, and Shaou-Gang Miaou. "Arithmetic Means of Accuracies: A Classifier Performance Measurement for Imbalanced Data Set." Proceedings of the International Conference on Audio, Language and Image Processing (ICALIP 2010), Shanghai IEEE, 2010. 1244 - 1251.

BibTeX: 

Zuletzt aktualisiert 2018-18-10 um 22:00