Hi

There are many ways of doing it:

You can use SMOTE method for the same.

Thank you.

Hi Jean, 

Some suggestions: 

(1) Duplicate features

(2) Combine two (or more) continuous features into one (e.g. adding or multiplying them). 

Best Regards,

Heitor

Here is a paper which may help.

"An evaluation framework for input variable selection algorithms for

environmental data-driven models"

Article An evaluation framework for input variable selection algorit...

Sometimes, redundancy may increase query speed, it is a trade off between space and time.

Hello everyone,

As far as I know, SMOTE is used for instance selection, isn't it? How should I use it in the sense of adding new features (attributes) to my dataset?

Cheers,

I could recommend you a paper and the corresponding code for matlab. The name is: " infinite feature selection ". It is perfect to detect redundant features. In this case, by simply inverting the basic function  (based on correlation and dispersion) you will obtain a ranking of variables by redundancy. I mean before weighting the graph by fun you could put 1-fun on the edges. 

You can use existing datasets, or create synthetic data as we did in the paper to generate synthetic features in order to test your technique. 

By ranking the features just select the first ones of this inverted version on inf-FS.

The project page is here http://giorgioroffo.it