Aaronne
Posts:
110
Registered:
6/2/11


How to do a classification using Matlab?
Posted:
Mar 14, 2013 2:46 PM


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

