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I have a spectrogram of a signal and want to classify it. I used different features such as MFFC, the density of power spectrum and other common features and classify using the random forest, but I did not get good results.

I think that my features are not well selected and I guess that there is no relation between my aimed classes and the selected features. my classes are so rare.

could anyone suggest a way for obtaining features?(for very rare data) also, I used some methods of feature extraction from biological images, but I think that the data are different. my data are double and those are some 3 dimensions data.

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Try one of these algorithms for feature selection:

BLogReg
CFS
Chi Square
FCBF
Fisher Score
Gini Index
Information Gain
Kruskal-Wallis
mRMR
Relief-F
SBMLR
T-test
SPEC

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  • $\begingroup$ Link-only answers aren't all that great. Could you please add a few bullet points to your answer, listing the algorithms from the link? $\endgroup$ – Marcus Müller Mar 31 '17 at 9:48

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