多输出支持向量机(multioutput svm)

function RESULTS = assessment(Labels,PreLabels,par) % % function RESULTS = assessment(Labels,PreLabels,par) % % INPUTS: % % Labels : A vector containing the true (actual) labels for a given set of sample. % PreLabels : A vector containing the estimated (predicted) labels for a given set of sample. % par : 'class' or 'regress' % % OUTPUTS: (all contained in struct RESULTS) % % ConfusionMatrix: Confusion matrix of the classification process (True labels in columns, predictions in rows) % Kappa : Estimated Cohen's Kappa coefficient % OA : Overall Accuracy % varKappa : Variance of the estimated Kappa coefficient % Z : A basic Z-score for significance testing (considering that Kappa is normally distributed) % CI : Confidence interval at 95% for the estimated Kappa coefficient % Wilcoxon, sign test and McNemar's test of significance differences % % Gustavo Camps-Valls, 2007(c) % gcamps@uv.es % % Formulae in: % Assessing the Accuracy of Remotely Sensed Data % by Russell G Congalton, Kass Green. CRC Press % switch lower(par) case {'class'} Etiquetas = union(Labels,PreLabels); % Class labels (usually, 1,2,3.... but can work with text labels) NumClases = length(Etiquetas); % Number of classes % Compute confusion matrix ConfusionMatrix = zeros(NumClases); for i=1:NumClases for j=1:NumClases ConfusionMatrix(i,j) = length(find(PreLabels==Etiquetas(i) & Labels==Etiquetas(j))); end; end; % Compute Overall Accuracy and Cohen's kappa statistic n = sum(ConfusionMatrix(:)); % Total number of samples PA = sum(diag(ConfusionMatrix)); OA = PA/n; % Estimated Overall Cohen's Kappa (suboptimal implementation) npj = sum(ConfusionMatrix,1); nip = sum(ConfusionMatrix,2); PE = npj*nip; if (n*PA-PE) == 0 && (n^2-PE) == 0 % Solve indetermination warning('0 divided by 0') Kappa = 1; else Kappa = (n*PA-PE)/(n^2-PE); end % Cohen's Kappa Variance theta1 = OA; theta2 = PE/n^2; theta3 = (nip'+npj) * diag(ConfusionMatrix) / n^2; suma4 = 0; for i=1:NumClases for j=1:NumClases suma4 = suma4 + ConfusionMatrix(i,j)*(nip(i) + npj(j))^2; end; end; theta4 = suma4/n^3; varKappa = ( theta1*(1-theta1)/(1-theta2)^2 + 2*(1-theta1)*(2*theta1*theta2-theta3)/(1-theta2)^3 + (1-theta1)^2*(theta4-4*theta2^2)/(1-theta2)^4 )/n; Z = Kappa/sqrt(varKappa); CI = [Kappa + 1.96*sqrt(varKappa), Kappa - 1.96*sqrt(varKappa)]; if NumClases==2 % Wilcoxon test at 95% confidence interval [p1,h1] = signrank(Labels,PreLabels); if h1==0 RESULTS.WilcoxonComment = 'The null hypothesis of both distributions come from the same median can be rejected at the 5% level.'; elseif h1==1 RESULTS.WilcoxonComment = 'The null hypothesis of both distributions come from the same median cannot be rejected at the 5% level.'; end; RESULTS.WilcoxonP = p1; % Sign-test at 95% confidence interval [p2,h2] = signtest(Labels,PreLabels); if h2==0 RESULTS.SignTestComment = 'The null hypothesis of both distributions come from the same median can be rejected at the 5% level.'; elseif h2==1 RESULTS.SignTestComment = 'The null hypothesis of both distributions come from the same median cannot be rejected at the 5% level.'; end; RESULTS.SignTestP = p2; % McNemar RESULTS.Chi2 = (abs(ConfusionMatrix(1,2)-ConfusionMatrix(2,1))-1)^2/(ConfusionMatrix(1,2)+ConfusionMatrix(2,1)); if RESULTS.Chi2<10 RESULTS.Chi2Comments = 'The Chi^2 is not approximated by the chi-square distribution. Instead, the sign test should be used.'; else RESULTS.Chi2Comments = 'The Chi^2 is approximated by the chi-square distribution. The greater Chi^2, the lower p<0.05 and thus the difference is more statistically significant.'; end end % Store results: RESULTS.ConfusionMatrix = ConfusionMatrix; RESULTS.Kappa = Kappa; RESULTS.OA = 100*OA; RESULTS.varKappa = varKappa; RESULTS.Z = Z; RESULTS.CI = CI; case 'regress' RESULTS.ME = mean(Labels-PreLabels); RESULTS.RMSE = sqrt(mean((Labels-PreLabels).^2)); RESULTS.MAE = mean(abs(Labels-PreLabels)); [rr, pp] = corrcoef(Labels,PreLabels); RESULTS.R = rr(1,2); RESULTS.RP = pp(1,2); otherwise disp('Unknown learning paradigm.') end