Got Questions? Get Answers.
Discover MakerZone

MATLAB and Simulink resources for Arduino, LEGO, and Raspberry Pi

Learn more

Discover what MATLAB® can do for your career.

Opportunities for recent engineering grads.

Apply Today

Thread Subject:
Evaluating the contribution of variables to a classifier

Subject: Evaluating the contribution of variables to a classifier

From: Rob Campbell

Date: 9 Apr, 2010 14:30:23

Message: 1 of 1

Hi,

I am running classifiers (e.g. MDA or SVM) on a data set with many explanatory variables (usually between 60 and 150). In general, I am dividing data into 3 to 7 groups.

Many of the explanatory variables will contribute only noise and so I would like to know which variables are important for correctly classifying the data. I have tried running the classifier with each variable removed in turn but the results are confusing. *Removal* of variables which have high signal to noise ratios (these should be the informative cases) can sometimes result in an *increase* in classification accuracy. This makes me think that there is substantial information in the correlations between variables. What would be a good way of testing this? I can't do it exhaustively because there are too many variable combinations. Could the optimization toolbox help?

Cheers

Tags for this Thread

No tags are associated with this thread.

What are tags?

A tag is like a keyword or category label associated with each thread. Tags make it easier for you to find threads of interest.

Anyone can tag a thread. Tags are public and visible to everyone.

Contact us