How to do a classification using Matlab?

Hi Smart Guys,

I have got the data (can be downloaded here: [enter link description here][1]) and tried to run a simple LDA based classification based on the 11 features stored in the dataset, ie, F1, F2, ..., F11.

Here I wrote some codes in Matlab using only 2 features. May I ask some questions based on the codes I have got please?

    clc; clf; clear all; close all;
    
    %% Load the extracted features
    features = xlsread('ExtractedFeatures.xls');
    numFeatures = 23;
    
    %% Define ground truth
    groundTruthGroup = cell(numFeatures,1);
    groundTruthGroup(1:15) = cellstr('Good');
    groundTruthGroup(16:end) = cellstr('bad');
    
    %% Select features
    featureSelcted = [features(:,3), features(:,9)];
    
    %% Run LDA
    [ldaClass, ldaResubErr] = classify(featureSelcted(:,1:2), featureSelcted(:,1:2), groundTruthGroup, 'linear');
    bad = ~strcmp(ldaClass,groundTruthGroup);
    ldaResubErr2 = sum(bad)/numFeatures;
    
    [ldaResubCM,grpOrder] = confusionmat(groundTruthGroup,ldaClass);
    
    %% Scatter plot
    gscatter(featureSelcted(:,1), featureSelcted(:,2), groundTruthGroup, 'rgb', 'osd');
    xlabel('Feature 3');
    ylabel('Feature 9');
    hold on;
    plot(featureSelcted(bad,1), featureSelcted(bad,2), 'kx');
    hold off;
    
    %% Leave one out cross validation
    leaveOneOutPartition = cvpartition(numFeatures, 'leaveout');
    ldaClassFun = @(xtrain, ytrain, xtest)(classify(xtest, xtrain, ytrain, 'linear'));
    ldaCVErr = crossval('mcr', featureSelcted(:,1:2), ...
        groundTruthGroup, 'predfun', ldaClassFun, 'partition', leaveOneOutPartition);
    
    %% Display the results
    clc;
    disp('______________________________________ Results ______________________________________________________');
    disp(' ');
    disp(sprintf('Resubstitution Error of LDA (Training Error calculated by Matlab build-in): %d', ldaResubErr));
    disp(sprintf('Resubstitution Error of LDA (Training Error calculated manually): %d', ldaResubErr2));
    disp(' ');
    disp('Confusion Matrix:');
    disp(ldaResubCM)
    disp(sprintf('Cross Validation Error of LDA (Leave One Out): %d', ldaCVErr));
    disp(' ');
    disp('______________________________________________________________________________________________________');


I. My first question is how to do a feature selection? For example, using forward or backward feature selection, and t-test based methods?

I have checked that the Matlab has got the `sequentialfs` method but not sure how to incorporate it into my codes.

II. How do using the Matlab `classify` method to do a classification with more than 2 features? Should we perform the PCA at first? For example, currently we have 11 features, and we run PCA to produce 2 or 3 PCs and then run the classification? (I am expecting to write a loop to add each feature one by one to do a forward feature selection. Not just run PCA to do a dimension reduciton.)

III. I have also try to run a ROC analysis. I refer to the webpage [enter link description here][2] which has got an implementation of a simple LDA method and produce the linear scores of the LDA. Then we can use `perfcurve` to get the ROC curve.

IIIa. However, I am not sure how to use `classify` method with `perfcurve` to get the ROC.

IIIb. Also, how to do a ROC with the cross-validation?

IIIc. After we have got the `OPTROCPT`, which is the best cut-off point, how can we use this cut-off point to produce better classification?

    %% ROC Analysis
    featureSelcted = [features(:,3), features(:,9)];
    groundTruthNumericalLable = [zeros(15,1); ones(8,1)];
    
    % Calculate linear discriminant coefficients
    ldaCoefficients = LDA(featureSelcted, groundTruthNumericalLable);
    
    % Calulcate linear scores for the training data
    ldaLinearScores = [ones(numFeatures,1) featureSelcted] * ldaCoefficients';
    
    % Calculate class probabilities
    classProbabilities = exp(ldaLinearScores) ./ repmat(sum(exp(ldaLinearScores),2),[1 2]);
    
    % Fit probabilities for scores
    figure,
    [FPR, TPR, Thr, AUC, OPTROCPT] = perfcurve(groundTruthNumericalLable(:,1), classProbabilities(:,1), 0);
    plot(FPR, TPR, 'or-')
    xlabel('False positive rate (FPR, 1-Specificity)'); ylabel('True positive rate (TPR, Sensitivity)')
    title('ROC for classification by LDA')
    grid on;

IV. Currently, I calculate the accuracy of the training and cross validation errors by the classify and `crossval` functions. May I ask how to get those values in a summary by using `classperf`?

V. If anyone knows a good tutorial of using Matlab statistic toolbox to do machine learning task with a full example please tell me.

Some Matlab Help examples are really confusing to me because the examples are made in pieces and I am really a novice to machine learning. Sorry if I asked some question bot proper. Thanks very much for your help.



A.


  [1]: http://ge.tt/6eijw4b/v/0
  [2]: http://matlabdatamining.blogspot.co.uk/2010/12/linear-discriminant-analysis-lda.html