1 research outputs found
Knowledge Representation in Learning Classifier Systems: A Review
Knowledge representation is a key component to the success of all rule based
systems including learning classifier systems (LCSs). This component brings
insight into how to partition the problem space what in turn seeks prominent
role in generalization capacity of the system as a whole. Recently, knowledge
representation component has received great deal of attention within data
mining communities due to its impacts on rule based systems in terms of
efficiency and efficacy. The current work is an attempt to find a comprehensive
and yet elaborate view into the existing knowledge representation techniques in
LCS domain in general and XCS in specific. To achieve the objectives, knowledge
representation techniques are grouped into different categories based on the
classification approach in which they are incorporated. In each category, the
underlying rule representation schema and the format of classifier condition to
support the corresponding representation are presented. Furthermore, a precise
explanation on the way that each technique partitions the problem space along
with the extensive experimental results is provided. To have an elaborated view
on the functionality of each technique, a comparative analysis of existing
techniques on some conventional problems is provided. We expect this survey to
be of interest to the LCS researchers and practitioners since it provides a
guideline for choosing a proper knowledge representation technique for a given
problem and also opens up new streams of research on this topic