Introduction
Subset selection: The use of labeled examples to induce a model that classifies objects into a finite set of known classes is one of the main challenges of supervised classification in machine learning. Vectors of numeric or nominal features are used to describe the various examples. In the feature subset selection problem, a learning algorithm is faced with the problem of selecting some subset of features upon which to focus its attention, while ignoring the rest.
When fitting a linear regression model, a subset of variables that best describe the data are of interest. There are a number of different ways the best subset, applying a number of different strategies, can be adopted when searching for a variables set. If there are m variables and the best regression model consists of p variables, p≤m, then a more general approach to pick the best subset might be to try all possible combinations of p variables and select the model that fits the data the best.
However, there...
