Contents

Description of demo_ordinal_kernel.m

Shows performance of kernelized multiclass softmax classification, SSVM multiclass classification, kernelized ordinal logistic regression, and kernelized ordinal regression for multiclass classification

clear all
close all
generateData_ordinal_2D

usage of ordinal regression for classification, polynomial kernel

kernelOptions = [];
kernelOptions.d = 2;
kernelOptions.bias = 1;
subOptions = [];
subOptions.kernelFunc = @ml_kernel_poly;
subOptions.kernelOptions = kernelOptions;
options = struct('nClasses',nClasses,...
                 'regressionFunc', @ml_regression_kernel, ...
                 'subOptions', subOptions);
model = ml_ordinal_regression(Xtrain,ytrain,options);
yhatTest = model.predict(model, Xtest);
yhatTrain = model.predict(model, Xtrain);
testError = sum(ytest~=yhatTest)/length(ytest);
model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model.name, testError);
linear_makeOneContourPlot(Xtrain,ytrain, model)

Averaged misclassification test error with Ordinal LS Regression, Polynomial Kernel is: 0.256

usage of ordinal regression for classification, rbf kernel

kernelOptions = [];
kernelOptions.sigma = 1;
subOptions = [];
subOptions.kernelFunc = @ml_kernel_rbf;
subOptions.kernelOptions = kernelOptions;
options = struct('nClasses',nClasses,...
                 'regressionFunc', @ml_regression_kernel, ...
                 'subOptions', subOptions);
model = ml_ordinal_regression(Xtrain, ytrain, options);
yhatTest = model.predict(model, Xtest);
yhatTrain = model.predict(model, Xtrain);
testError = sum(ytest~=yhatTest)/length(ytest);
model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model.name, testError);
linear_makeOneContourPlot(Xtrain,ytrain, model)

Averaged misclassification test error with Ordinal LS Regression, RBF Kernel is: 0.244

usage of ordinal logistic classification, rbf kernel

kernelOptions = [];
kernelOptions.sigma = 1;
trainFunc = @ml_kernel_ordinal_logistic;
options = struct('nClasses', nClasses,...
                 'kernelFunc', @ml_kernel_rbf, ...
                 'kernelOptions', kernelOptions);
model = ml_kernel_ordinal_logistic(Xtrain, ytrain, options);
yhatTest = model.predict(model, Xtest);
yhatTrain = model.predict(model, Xtrain);
testError = sum(ytest~=yhatTest)/length(ytest);
model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model.name, testError);
linear_makeOneContourPlot(Xtrain,ytrain, model)

Averaged misclassification test error with Ordinal Logistic Regression, RBF Kernel is: 0.040

usage of softmax classification, rbf kernel

kernelOptions = [];
kernelOptions.sigma = 1;
options = struct('nClasses',nClasses, ...
                 'kernelFunc',@ml_kernel_rbf, ...
                 'kernelOptions',kernelOptions);
model = ml_multiclass_kernel_softmax(Xtrain,ytrain,options);
yhatTest = model.predict(model, Xtest);
yhatTrain = model.predict(model, Xtrain);
testError = sum(ytest~=yhatTest)/length(ytest);
model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model.name, testError);
linear_makeOneContourPlot(Xtrain,ytrain, model)

Averaged misclassification test error with RBF Kernel Classification, Softmax Loss is: 0.112

usage of SSVM loss for classification, polynomial kernel

kernelOptions = [];
kernelOptions.d = 2;
kernelOptions.bias = 1;
options = struct('nClasses',nClasses, ...
                    'kernelFunc',@ml_kernel_poly, ...
                    'kernelOptions',kernelOptions,...
                    'link','ssvm');
model = ml_multiclass_kernel_softmax(Xtrain,ytrain,options);
yhatTest = model.predict(model, Xtest);
yhatTrain = model.predict(model, Xtrain);
testError = sum(ytest~=yhatTest)/length(ytest);
model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model.name, testError);
linear_makeOneContourPlot(Xtrain,ytrain, model)

Averaged misclassification test error with Polynomial Kernel Classification, SSVM Loss is: 0.164

Published with MATLAB® R2015a