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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 Arizona State - Feature Selection 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? $\endgroup$ Commented Mar 31, 2017 at 9:48
  • $\begingroup$ The link doesn't work. $\endgroup$ Commented May 3, 2022 at 8:54
  • $\begingroup$ @EricJohnson jundongl.github.io/scikit-feature/OLD/algorithms_old.html $\endgroup$
    – Sofiane
    Commented May 4, 2022 at 9:17

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