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!
