Alex Byrley

2017-Jun-29 17:25 UTC

### [R] Packages for Learning Algorithm Independent Branch and Bound for Feature Selection

I am looking for packages that can run a branch-and-bound algorithm to maximize a distance measure (such as Bhattacharyya or Mahalanobis) on a set of features. I would like this to be learning algorithm independent, so that the method just looks at the features, and selects the subset of a user-defined size that maximizes a distance criteria such as those stated above. Can anyone give some suggestions? Alex Byrley Graduate Student Department of Electrical Engineering 235 Davis Hall (716) 341-1802 [[alternative HTML version deleted]]

Enrico Schumann

2017-Jul-01 07:53 UTC

### [R] Packages for Learning Algorithm Independent Branch and Bound for Feature Selection

On Thu, 29 Jun 2017, Alex Byrley writes:> I am looking for packages that can run a branch-and-bound algorithm to > maximize a distance measure (such as Bhattacharyya or Mahalanobis) on a set > of features. > > I would like this to be learning algorithm independent, so that the method > just looks at the features, and selects the subset of a user-defined size > that maximizes a distance criteria such as those stated above. > > Can anyone give some suggestions? > > Alex Byrley > Graduate Student > Department of Electrical Engineering > 235 Davis Hall > (716) 341-1802 >It seems you are looking for a generic optimisation algorithm; so perhaps start at the task view: https://cran.r-project.org/web/views/Optimization.html What you describe is a combinatorial problem: select k from N features, with k (much) smaller than N. So I'd suggest to also look at heuristic algorithms that can deal with such problems (e.g. genetic algorithms). -- Enrico Schumann Lucerne, Switzerland http://enricoschumann.net