Hi there!
I'm a masterstudent in bio-engineering and I'm currently working on my thesis, which includes a lot of data processing. I have 66 features of a signal in about 248 windows (each 5 seconds), which are the input of my classifier. The ouput is the level of a specific symptom, an ordinal variable. I wanted to apply 'feature selection' before the classification because I want to know what physical parameters correlate well with the symptom.
I was thinking about using the 'predictor screening' option of JMP. But to do this, I need to understand 2 extra things.
- can this 'predictor screening' be used as a general feature selection tool? After feature selection, I want to try different classifiers, not just bootstrap tree forest (which is used in the predictor screening tool).
- How do I exactly interpret the 'contribution' level of the output of predictor screening? I've found a lot of confusing information about this.
Extra remark; if you have any other ideas for feature selection, let me know! :)
Have a nice day,
Ben