My blog has moved! Redirecting...

You should be automatically redirected. If not, visit http://www.dataminingblog.com and update your bookmarks.

Data Mining Research - dataminingblog.com: Feature selection

I'm a Data Miner Collection (T-shirts, Mugs & Mousepads)

All benefits are given to a charity association.

Wednesday, February 07, 2007

Feature selection

One of the most interesting and well written paper I have read regarding data mining is certainly "An Introduction to Variable and Feature Selection" (Guyon and Elisseeff, 2003). It is freely available on the Journal of Machine Learning Research website. After reading this paper, you should have a good view of what feature selection really is about. Although not popularized, the paper is written in a very readable way.

Feature selection may be useful for facilitating data visualization, reducing storage requirements and increasing performances of learning algorithms. The paper starts by a checklist of crucial points to discuss before applying any learning algorithm on your data. Then, topics such as variable ranking and variable subset selection are covered. A clear distinction is made between three different techniques for variable selection: wrappers, filters and embedded methods.

The article continues on dimensionality reduction and validation techniques in the case of variable selection. Finally examples of open problems are outlined. I have read several papers in data mining and related topics, and this is certainly the most comprehensive and readable one. In addition to the paper, and for more details about Matlab implementation, you can have a look at this post on Will's blog.

Sphere: Related Content

5 comments:

Will Dwinnell said...

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.

damien said...
This comment has been removed by the author.
damien said...

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

Sandro Saitta said...

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

photoshop image clipping path services said...

Nice post. Thanks.

 
Clicky Web Analytics