0
$\begingroup$

I'm try to study the correlation-based feature selection (cfs) form http://www.cs.waikato.ac.nz/~mhall/thesis.pdf but I'm not sure the relation between cfs and Symmetrical uncertainty (SU) theory, If I calculate the value of correlation, then I need to calculate the value of SU?

I don't understand how to choose number of feature after selected.

$\endgroup$
1
$\begingroup$

The feature selection method presented in the paper uses a correlation measure to compute the feature-class and feature-feature correlation. The paper experiments with three correlation measures (see chapter 4.2):

  1. Symmetrical Uncertainty
  2. Relief
  3. Minimum Description Length

So Symmetrical Uncertainty (SU) is just a correlation measure, you can use any correlation measure you like.

You use this correlation measure to compute the "merit" of a feature subset:

$M_S=\frac{k\bar{r_{cf}}}{\sqrt{k+k(k-1)\bar{r_{ff}}}}$

where

  • k is the number of features
  • $\bar{r_{cf}}$ is the mean class-feature correlation
  • $\bar{r_{ff}}$ is the mean feature-feature correlation

There are many ways to use $M_S$. The paper talks about forward search (start with an empty set and add features) or backward search (start with a set containing all the features and remove features).

So, you decide how many features you want and add/remove features until you remain with the desired number of features.

$\endgroup$
  • 1
    $\begingroup$ Welcome to Cross Validated! Thanks for posting, but are you really answering the question? $\endgroup$ – Scortchi - Reinstate Monica Oct 20 '17 at 12:51
  • $\begingroup$ @Scortchi you're right - I missed one of the questions. I reformulated to be more explicit and answered the other question. $\endgroup$ – berendeanicolae Oct 21 '17 at 14:20

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.