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How to get ROC curve using SVM?

20 vues (au cours des 30 derniers jours)
Imran Riaz
Imran Riaz le 3 Nov 2022
Réponse apportée : Rohit le 22 Fév 2023
I have extracted the features of finger knuckles using LBP and now want to classify using SVM. I am using following code and I get values of F1 score , recall, percision, and accuracy. Now I also want to plot ROC curve. Plz help me which changes are required to get ROC for one-vs-all classicification using SVM. Feat file is attached.
clc
clear
close all
%%
Data = load ("Feat13.mat");
Features = Data.Feat (:,1:end-1);
Labels = Data.Feat (:,end);
% Features = Data.Feat (1:200,1:end-1); % for 1st 200 only
% Labels = Data.Feat (1:200,end);
% [m,n] = size(Features);
[m,n] = size(Data.Feat);
P = 0.50;
idx = randperm(m) ;
Training = Data.Feat(idx(1:round(P*m)),:);
Testing = Data.Feat(idx(round(P*m)+1:end),:);
Train_Features = Training(:,1:end-1);
Train_Labels = Training(:,end);
Test_Features = Testing(:,1:end-1);
Test_Labels = Testing(:,end);
rng(1); % For reproducibility
% SVMModel = fitcecoc(Train_Features,Train_Labels);
t = templateSVM('Standardize',true,'KernelFunction','linear');
SVMModel = fitcecoc(Train_Features,Train_Labels,'Learners',t);
error = resubLoss(SVMModel)
[Pred_TrainLabels,Pred_TrainScore] = predict(SVMModel,Train_Features);
[Pred_TestLabels,Pred_TestScore] = predict(SVMModel,Test_Features);
% ROC_data = roc_curve(Test_Labels,Pred_TestLabels)
% [Pred_WholeLabels,Pred_WholeScore] = predict(SVMModel,Features);
% ROC_data = roc_curve(Labels,Pred_wholeLabels)
% ROC_data = roc_curve(Labels,Pred_WholeLabels)
%
% [X,Y,T,AUC] = perfcurve(Labels,Pred_WholeScore,'5')
% plot(X,Y)
%% % For whole labels and scores
% [tpr,fpr,thresholds] = roc(Labels,Pred_WholeLabels);
% plotroc(Labels,Pred_WholeScore())
% %% For Test labels and scores only
% [tpr,fpr,thresholds] = roc(Labels,Pred_TestLabels);
% plotroc(Labels,Pred_TestScore())
% [c_matrixp,Result]= confusion.getMatrix(Test_Labels,Pred_TestLabels);
% [c_matrixp,Result]= confusion.getMatrix(Labels,Pred_WholeLabels);
fig = figure;
cm = confusionchart(Test_Labels,Pred_TestLabels,'RowSummary','row-normalized','ColumnSummary','column-normalized');
% cm = confusionchart(Labels,Pred_WholeLabels,'RowSummary','row-normalized','ColumnSummary','column-normalized');
cm.Title = 'Finger Creases Classification Using SVM';
cm.RowSummary = 'row-normalized';
cm.ColumnSummary = 'column-normalized';
[m, order] = confusionmat(Test_Labels,Pred_TestLabels);
Diagonal=diag(m);
sum_rows=sum(m,2);
Precision=Diagonal./sum_rows;
Overall_Precision=mean(Precision)
sum_col=sum(m,1);
recall=Diagonal./sum_col';
recall(isnan(recall))=0;
overall_recall=mean(recall)
F1_Score=2*((Overall_Precision*overall_recall)/(Overall_Precision+overall_recall))
accuracy = sum(Test_Labels == Pred_TestLabels,'all')/numel(Pred_TestLabels)

Réponses (1)

Rohit
Rohit le 22 Fév 2023
You can compute a ROC curve and other performance curves by creating a rocmetrics object which support both binary and multiclass classification.
Refer to the below documentation links for further reference-

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