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Topic: How to do a classification using Matlab?
Replies: 4   Last Post: May 1, 2014 2:57 AM

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Alan Weiss

Posts: 1,261
Registered: 11/27/08
Re: How to do a classification using Matlab?
Posted: Mar 15, 2013 9:23 AM
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On 3/14/2013 2:46 PM, Aaronne wrote:
> 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


It sounds as if you have Statistics Toolbox. If so, then why bother
rewriting discriminant analysis code? There is a good deal of
information about discriminant analysis here:
http://www.mathworks.com/help/stats/discriminant-analysis-1.html
There may be more information than you care to read about classification
in these two sections:
http://www.mathworks.com/help/stats/supervised-learning.html
http://www.mathworks.com/help/stats/ensemble-learning.html

Good luck,

Alan Weiss
MATLAB mathematical toolbox documentation



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