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

Timotius I, Miaou SG (2010)


Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2010

Publisher: IEEE

Pages Range: 1244 - 1251

Conference Proceedings Title: International Conference on Audio, Language and Image Processing (ICALIP 2010)

Event location: Shanghai CN

ISBN: 978-1-4244-5856-1

URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5685124

DOI: 10.1109/ICALIP.2010.5685124

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.

Authors with CRIS profile

How to cite

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: Download