DocumentCode
2023972
Title
Arithmetic means of accuracies: A classifier performance measurement for imbalanced data set
Author
Timotius, Ivanna K. ; Shaou-Gang Miaou
Author_Institution
Dept. of Electron. Eng., Satya Wacana Christian Univ., Salatiga, Indonesia
fYear
2010
fDate
23-25 Nov. 2010
Firstpage
1244
Lastpage
1251
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.
Keywords
pattern classification; random processes; classification algorithm; classifier performance measurement; cost-sensitive classification; imbalanced data set; multiple-class classification; random guessing behavior; Accuracy; Algorithm design and analysis; Classification algorithms; Equations; Extraterrestrial measurements; Measurement uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Audio Language and Image Processing (ICALIP), 2010 International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-5856-1
Type
conf
DOI
10.1109/ICALIP.2010.5685124
Filename
5685124
Link To Document