A tool to look at classifier results and performs sensitivity, specificity, and ROC analysis, and produce curves suitable for publications.
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README.MD
ROI_error_bars.png
plot_roc_new.m
read_processing_results.m
run_analysis.m
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stats.m
stats_tool.fig
stats_tool.m

README.MD

Statistical Evaluation Tool for Classifiers

Tool to perform sensitivity, specificity, ROC curve, and AUC computation on classifier results, and generate averaged plots and AUC values for cross-testing.

Author: Rohit Rawat (rohit.rawat@mavs.uta.edu)
Created: 06/26/2012
Updated: 08/26/2015

Image Processing and Neural Networks Lab, UT Arlington

MLP Classifiers (recommended), RBF Classifiers, and NuClass (NuClass is now deprecated) can be obtained from: http://www.uta.edu/faculty/manry/

Run the script stats_tool.m to start the GUI. (Do not open the.fig file!). The GUI is self explanatory. The command line version is also included.

Screenshot Average ROI with error bars

Help

This tool accepts processing results files generated by classifiers, and performs sensitivity, specificity, ROC curve, and AUC computation.

  • Run the MOLF-ADAPT, MOLF-ADAPT-MERGE, RBF, or NuClass programs to train a neural network.
  • Then run the corresponding testing/processing programs to produce the processing result files for each cross-validation data set, saving them under different names.
  • Click the Add files button to add these files to the list of files. The remove and clear buttons work as advertised.
  • Starting the analysis will produce an ROC curve and AUC value for each file. The averaged ROC curve, and average AUC values will also be displayed. The tool can also be run for just a single file if cross-testing is not performed.

ROC plot options

  • To display the ROC plot for each validation file, select "Each plot in new window". To only display the averaged plot, select the other option. The averaged plot will be displayed in both the cases.
  • To save the displayed plots as JPEG files in the current directory, check the box underneath. The JPEG files will have the same names as the processing results files, but with a JPEG extension. The averaged plot is saved as "averaged_ROC_plot.jpg".
  • The positive class label should be set to 1 or 2, depending on what label has been used for the positive class. The negative class (or the control class) is automatically assigned the other label.

Examples

For an application in producing cross-validation ROC curves, please see: A. Yennu, F. Tian, H. Liu, R. Rawat, M. T. Manry, and R. Gatchel, “A preliminary investigation of human frontal cortex under noxious thermal stimulation over the temporomandibular joint using functional near infrared spectroscopy,” Journal of Applied Biobehavioral Research, vol. 18, no. 3, pp. 134–155, 2013.