This chapter presents an approach to feature selection that is quite different from most other selection methods. In most applications, measured values of predictor variables (features) are directly associated with measured values of target variables (which may be numeric or class membership). Traditional feature selection looks for demonstrable relationships between predictor candidates and one or more targets, treating each case (set of measured values) as an independent sample.
In this chapter, we look at a powerful feature selection algorithm in which ...
