Title | Adapting the Fitness Function in GP for Data Mining |
Publication Type | Conference Paper |
Year of Publication | 1999 |
Authors | Eggermont, J, Eiben, AE, van Hemert, JI |
Conference Name | Springer Lecture Notes on Computer Science |
Publisher | Springer-Verlag, Berlin |
Editor | Poli, R, Nordin, P, Langdon, WB, Fogarty, TC |
ISBN Number | 3-540-65899-8 |
Keywords | data mining; genetic programming |
Abstract | In this paper we describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in EAs for constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is well suited for data mining tasks where the fitness of a candidate solution is composed by `local scores' on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are. |
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