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In this article, the editor introduces in detail "how to use svmtrain for data classification and prediction". The content is detailed, the steps are clear, and the details are handled properly. I hope this "how to use svmtrain for data classification and prediction" article can help you solve your doubts.

After installing the libsvm toolkit, use svmtrain for data classification and prediction

% clear environment variables

Close all

Clear

Clc

Format compact

%% data extraction

% load test data wine

% contains data as classnumber = 3

Matrix of% wine:178*13

Column vector of% wine_labes:178*1

Load wine.mat

%% draw box visualization diagram of test data

Figure

Boxplot (wine,'orientation','horizontal','labels',categories)

Title ('box visualization of wine data', 'FontSize',12)

Xlabel ('attribute value', 'FontSize',12)

Grid on

%% draw the fractal dimension visualization diagram of the test data

Figure

Subplot (3, 5, 1)

Hold on

For run = 1PUR 178

Plot (run,wine_labels (run),'*')

End

Xlabel ('sample', 'FontSize',10)

Ylabel ('category tag', 'FontSize',10)

Title ('class','FontSize',10)

For run = 2:14

Subplot (3, 5, run)

Hold on

Str = ['attrib', num2str (run-1)]

For I = 1PUR 178

Plot (iGraine wine),'*')

End

Xlabel ('sample', 'FontSize',10)

Ylabel ('attribute value', 'FontSize',10)

Title (str,'FontSize',10)

End

Selected training set and test set

1-30 of the first category, 60-95 of the second category, 131-153 of the third category as the training set

Train_wine = [wine (1 wine 30:); wine (60 15); wine (131 15)]

The tags of the corresponding training sets should also be separated.

Train_wine_labels = [wine_labels (1:30); wine_labels (60:95); wine_labels (131 wine_labels 153)]

% take 31-59 of the first category, 96-130 of the second category, and 154-178 of the third category as the test set

Test_wine = [wine (31 wine 59:); wine (96 14 130:); wine (154 14: 178)]

The tags of the corresponding test sets should also be separated.

Test_wine_labels = [wine_labels (31:59); wine_labels (9614); wine_labels (154)]

%% data preprocessing

% data preprocessing to normalize the training set and test set to the [0d1] interval

[mtrain,ntrain] = size (train_wine)

[mtest,ntest] = size (test_wine)

Dataset = [train_wine;test_wine]

% mapminmax is the normalized function that comes with MATLAB

[dataset_scale,ps] = mapminmax (dataset',0,1)

Dataset_scale = dataset_scale'

Train_wine = dataset_scale (1)

Test_wine = dataset_scale (mtrain+1): (mtrain+mtest),:)

%% SVM network training

Model = svmtrain (train_wine_labels, train_wine,'- c 2-g 1')

% SVM Network Forecast

[predict_label, accuracy] = svmpredict (test_wine_labels, test_wine, model)

%% result analysis

Actual classification and prediction classification diagram of% test set

% from the chart, we can see that only one test sample was misclassified.

Figure

Hold on

Plot (test_wine_labels,'o')

Plot (predict_label,'r*')

Xlabel ('test set sample', 'FontSize',12)

Ylabel ('category tag', 'FontSize',12)

Legend ('actual test set classification', 'predictive test set classification')

Title ('actual and predictive classification diagrams of test sets', 'FontSize',12)

Grid on

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