Feature Selection for Regression Problems

Feature subset selection is the process of identifying and removing from a training data set as much irrelevant and redundant features as possible. This reduces the dimensionality of the data and may enable regression algorithms to operate faster and more effectively. In some cases, correlation coefficient can be improved; in others, the result is a more compact, easily interpreted representation of the target concept. This paper compares five well-known wrapper feature selection methods. Experimental results are reported using four well known representative regression algorithms.