Illinois Genetic Algorithms Laboratory

Wilson, S.W. (2000)
TR No.: 2000028 | Download PDF | Download PS

Abstract:
The classifier system XCS was investigated for data mining applications where the dataset discrimination surface (DS) is generally oblique to the attribute axes. Despite the classifiers’ hyper-rectangular predicates, XCS reached 100% performance on synthetic problems with diagonal DS’s and, in a train/test experiment, competitive performance on the Wisconsin Breast Cancer dataset. Final classifiers in an extended WBC learning run were interpretable to suggest dependencies on one or a few attributes. For data mining of numeric datasets with partially oblique discrimination surfaces, XCS shows promise from both performance and pattern discovery viewpoints.