Hello, Pietro. Thank you for having interest in the subject.
The algorithm itself is rather straightforward. Some challenge is to put it
into the framework of scikit-learn.
In the original paper they evaluated feature subsets using Mahalanobis distance
between classes, but it can be any other criterion: cross validation score of a
given estimator or the value of a target function of a given unsupervised
method. It's not completely clear how best to implement it.
I will keep your code in mind, meanwhile let's wait for a word from core
developers.
Nikolay Mayorov.
> Date: Thu, 9 Oct 2014 06:58:46 +0200
> From: [email protected]
> To: [email protected]
> Subject: Re: [Scikit-learn-general] Feature selection: floating search
> algorithm
>
> Hi Nikolay,
>
> On Wed, Oct 8, 2014 at 10:03 PM, Nikolay Mayorov <[email protected]> wrote:
> > Hi!
> >
> > Do you think scikit-learn will benefit from the general algorithm of feature
> > selection as described by P.Pudil et al. in "Floating search methods in
> > feature selection"?
> >
> > It is a wrapper method which alternates feature additions and removals
> > (starting from empty or full set of features.) It is known to find optimal
> > or close to optimal feature subset in efficient time.
> >
> > I want to implement it, if you are interested.
>
> I've just finished a first implementation of the Pudil algorithm few
> days ago, may be could be a start point.
>
> https://gist.github.com/zarch/abb3993c55fb61b834d1
>
> Have a nice day.
>
> Pietro
>
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