ON AUTOMATIC FEATURE SELECTION
Wojciech Siedlecki, Jack Sklansky · International Journal of Pattern Recognition and Artificial Intelligence · 1988
We review recent research on methods for selecting features for multidimensional pattern classification. These methods include nonmonotonicity-tolerant branch-and-bound search and beam search. We describe the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms. We compare these methods to facilitate the planning of future research on feature selection.