DocumentCode
595083
Title
Null QQ plots: A simple graphical alternative to significance testing for the comparison of classifiers
Author
Berrar, D.
Author_Institution
Tokyo Inst. of Technol., Yokohama, Japan
fYear
2012
fDate
11-15 Nov. 2012
Firstpage
1852
Lastpage
1855
Abstract
The evaluation of machine learning algorithms is commonly based on statistical significance tests. However, the suitability of such tests is often questionable. We propose null QQ plots as a simple yet powerful graphical alternative to significance testing. Using ten benchmark data sets, we demonstrate that these plots concisely summarize the essential results from a comparative classification study, while they are easy to produce and interpret.
Keywords
learning (artificial intelligence); pattern classification; statistical testing; comparative classification study; graphical alternative; machine learning algorithms; null QQ plots; significance testing; statistical significance tests; ten benchmark data sets; Accuracy; Data models; Machine learning; Machine learning algorithms; Single photon emission computed tomography; Sonar; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location
Tsukuba
ISSN
1051-4651
Print_ISBN
978-1-4673-2216-4
Type
conf
Filename
6460514
Link To Document