Comments on: Feature selection

By: Text Classification for Sentiment Analysis – Eliminate Low Information Features «streamhacker.com

Wed, 16 Jun 2010 21:22:17 +0000

[...] and the curse of dimensionality. When you use only the higher information features, you can increase performance while also decreasing the size of the model, which results in less memory usage along with faster [...]

By: Sandro Saitta

Sandro Saitta — Mon, 19 Feb 2007 19:02:00 +0000

Will and Damien, thanks for your complementary comments. I will have a look at this new book you mentioned as well as your blog.

By: damien

damien — Sat, 10 Feb 2007 20:56:00 +0000

This paper is indeed really good (it was the subject of one of the earliest posts on my blog)

The authors have just edited a book that is also very well written. The first part is an introduction to feature selection and the second part presents the results of the feature selection contest that was help in 2003. See the website of the book here : here

By: Will Dwinnell

Will Dwinnell — Thu, 08 Feb 2007 12:46:00 +0000

I've noticed that, among filter methods described in the literature, there seem to be 3 common approaches:

1. Seek correlation between individual predictors and the target and, simultaneously, lack of correlation among predictors (CFS, if I'm not mistaken).

2. Seek a group predictors which provide high separation of target classes (Fisher discriminant method, Weiss and Indurkhya's independent features).

3. Reduce predictors without regard to target (PCA, clustering of predictor variables).

Lately, I've been leaning heavily on my GA-driven implementation of Weiss and Indurkhya's approach (which seems to work very well for linear models), but am collecting a number of these techniques.