www.gusucode.com > ROC曲线仿真源码程序 > ROC曲线仿真源码程序/demo.m
% Demo for running linear descriminant analysis on example data and % plotting ROC curves with confidence intervals and AUC % B. Irving % load an example dataset. The fisheriris dataset contains 3 labels % ('virginica, versicolor, senosa) and features from thos species % http://www.mathworks.co.uk/products/demos/statistics/classdemo.html load fisheriris %loads species and meas % Classification 1 % creating [0, 1] labels for classifying virginica or not virginica labels = strcmp(species, 'virginica'); % Initialise the classifier class with the features and labels S2=SimpleClassifier(meas, labels, 'virginica'); % Run leave one out cross validation S2=S2.clas_LOOCV('linear'); % Display the confusion matrix of the LOOCV S2.disp_conf(); % Generate a ROC curve S2=S2.roc_curve_perf_pos(); % Classification 2 % creating [0, 1] labels for classifying versicolor or not versicolor labels = strcmp(species, 'versicolor'); S3=SimpleClassifier(meas, labels, 'versicolor'); S3=S3.clas_LOOCV('linear'); S3.disp_conf(); S3=S3.roc_curve_perf_pos(); % Plot as many ROC objects as you want plot(S2, S3)