Algorithms and methods for attribute/feature selection? I have data with continuous class and I'm searching for good methods to reduce number of attributes. Now I'm using correlation based filters, random forests and Gram–Schmidt  algorithm. 
What I want to achieve is answer which attributes are more important/relevant to class attribute than others. 
By using methods that I mentioned before I can reach this goal, but is there any other good algorithms worth noticing?
 A: My heart will be always with RF, but still you may take a look at Rough Sets. Especially LERS works quite good in case of massively disturbed data.
You may also try with importance obtained from other classifiers, like SVMs or Random Naive Bayes.
A: The Task view on Machine Learning and Statistical Learning is a good starting point for question like this.  
A: Regularised regression with an L1 penatly term has worked well for me (c.f. LASSO and LARS).
A: I have had good results with ensemble feature selection procedures. For implementation you can take a look at the Java-ML library.
For references see for example here.
I believe that these procedures are also readily availiable in R.
A: I'm a big fan of the rfe function in the caret package.  You can easily use it to cross-validate feature importance ratings from a random forest, a linear model, a bagged-tree model, a naive bayesian model, or any other algorithm that returns a measure of variable importance.
You can read more here.
A: Principal Component Analysis is a fairly common technique used to reduce the dimension of sampled data.  You can find a very good implementation in R.
