Mixed decision trees: Minimizing knowledge representation bias in LCS

Abstract

Learning classifier systems tend to inherit - a priori - a given knowledge representation language for expressing the concepts to learn. Hence, even before getting started, this choice biases what can be learned, becoming critical for some real-world applications like data mining. However, such bias may be minimized by hybridizing different knowledge representations via evolutionary mixing. This paper presents a first attempt to produce an evolutionary framework that evolves mixed decision trees of heterogeneous knowledge representations. © Springer-Verlag Berlin Heidelberg 2004.