Title :
Null QQ plots: A simple graphical alternative to significance testing for the comparison of classifiers
Author_Institution :
Tokyo Inst. of Technol., Yokohama, Japan
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;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4
