Matlab SVM linear binary classification failure

2 ビュー (過去 30 日間)
Ruben
Ruben 2016 年 4 月 18 日
コメント済み: Ilya 2016 年 4 月 28 日
I'm trying to implement a simple SVM linear binary classification in Matlab but I got strange results. I have two classes g={-1;1} defined by two predictors varX and varY. In fact, varY is enough to classify the dataset in two distinct classes (about varY=0.38) but I will keep varX as random variable since I will need it to other works.
Using the code bellow (adapted from MAtlab examples) I got a wrong classifier. Linear classifier should be closer to an horizontal line about varY=0.38, as we can perceive by ploting 2D points. It is not displayed the line that should separate two classes! What am I doing wrong?
g(1:14,1)=1;
g(15:26,1)=-1;
m3(:,1)=rand(26,1); %varX
m3(:,2)=[0.4008; 0.3984; 0.4054; 0.4048; 0.4052; 0.4071; 0.4088; 0.4113; 0.4189;
0.4220; 0.4265; 0.4353; 0.4361; 0.4288; 0.3458; 0.3415; 0.3528;
0.3481; 0.3564; 0.3374; 0.3610; 0.3241; 0.3593; 0.3434; 0.3361; 0.3201]; %varY
SVMmodel_testm = fitcsvm(m3,g,'KernelFunction','Linear');
d = 0.005; % Step size of the grid
[x1Grid,x2Grid] = meshgrid(min(m3(:,1)):d:max(m3(:,1)),...
min(m3(:,2)):d:max(m3(:,2)));
xGrid = [x1Grid(:),x2Grid(:)]; % The grid
[~,scores2] = predict(SVMmodel_testm,xGrid); % The scores
figure();
h(1:2)=gscatter(m3(:,1), m3(:,2), g,'br','ox');
hold on
% Support vectors
h(3) = plot(m3(SVMmodel_testm.IsSupportVector,1),m3(SVMmodel_testm.IsSupportVector,2),'ko','MarkerSize',10);
% Decision boundary
contour(x1Grid,x2Grid,reshape(scores2(:,1),size(x1Grid)),[0 0],'k');
xlabel('varX'); ylabel('varY');
set(gca,'Color',[0.5 0.5 0.5]);
hold off

採用された回答

Ilya
Ilya 2016 年 4 月 18 日
This is a consequence of the data being poorly scaled. Do std(m3) and observe that the standard deviations of the two predictors differ by an order of magnitude. Pass Standardize=true to fitcsvm.
  3 件のコメント
Ruben
Ruben 2016 年 4 月 20 日
How can we manage SVM model to achieve more false-positives and less false-negatives? Change Bias parameter is not allowed, is there other way to shift hyperplane towards positive or negative class? I know that parameter BoxConstraint can be adapted.
Ilya
Ilya 2016 年 4 月 28 日
Construct a ROC curve using the perfcurve function and find the optimal threshold.

サインインしてコメントする。

その他の回答 (0 件)

カテゴリ

Help Center および File ExchangeClassification についてさらに検索

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by