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Hello, I need your suggestion... I am doing some biomedical project. It is a calssification problem. There are two groups. One is 'Normal' and other is 'Cancer'. I have taken 30 subjects which is the mixture of 'Normal' and 'Cancer' for the analysis. I used a concept called "Recurrence Quantification" to get features. In that I got DWT coefficient. I applied Recurrence analysis for DWT coffiecient and computed 6 values RR,DET,LMAX,ENT,LAM,TT. My problem shown in figure.
This is the feature set I got by Rrecurrence Analysis. 6 features are RR,DET,LMAX,ENT,LAM,TT which I got for DWT coefficients (An, D1 - D7, Global) (9 rows) Coming to my classification problem, So I got 9x6 matrix for each subject in 30 peoples group. So I need an algorithm such that It has to classify these two groups from the mixture of 'Cancer' and 'Normal' by using these 9x6 matrix. Which algorithm is better? I heard that feature means values should be in vector from only. But mine is a 9x6 matrix. Then how can solve this? Can we do this by LDA or SVM or any other... suggest me... Thanks you guys!
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So you only have 30 training examples total? Also, are they labeled with the desired output (Normal vs. Cancer)?
No, I have to classify according to the values I got (9x6), instead of lables.
You want a classifier to predict the output label from the input values (9x6). Typically, one has examples of correct output labels for the given input values.
If you don't have any examples of correct output labels, then how are you going to know if your classifier works or not?
Ok, what algorithm works better, if we have labels?
How many labels would you have? 30? 1000?
There are 30 people(subjects) in the whole experiment. In which 15 are 'Normal' and other 15 are 'Cancer'. So, Two labels...
For each subject we get 9x6 matrix with corresponding label 'Normal' or 'Cancer'.
@Joseph Turian, Am I wrong with this interpretation ? Help me...