Search-based Class Discretization
Luís Torgo and João Gama
1997
Abstract
We present a methodology that enables the use
of classification algorithms on regression tasks. We
implement this method in system RECLA that transforms a
regression problem into a classification one and then
uses an existent classification system to solve this new
problem. The transformation consists of mapping a
continuous variable into an ordinal variable by grouping
its values into an appropriate set of intervals. We use
misclassification costs as a means to reflect the
implicit ordering among the ordinal values of the new
variable. We describe a set of alternative discretization
methods and, based on our experimental results, justify
the need for a search-based approach to choose the best
method. Our experimental results confirm the validity of
our search-based approach to class discretization, and
reveal the accuracy benefits of adding misclassification
costs.