Classify can handle any number of features. No need to reduce features. Not to say PCA as commented by Greg.
"Aaronne" wrote in message <kht5tf$m6v$1@newscl01ah.mathworks.com>...
> 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 buildin): %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 ttest 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 crossvalidation?
>
> IIIc. After we have got the `OPTROCPT`, which is the best cutoff point, how can we use this cutoff 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, 1Specificity)'); 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/lineardiscriminantanalysislda.html
