Improving the search of learning classifier systems through interpretable feature clustering
Hayden Andersen, Andrew Lensen, Will Neil Browne · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
Learning Classifier Systems (LCS) are a well-known machine learning method, producing sets of interpretable rules in order to solve a variety of problems. Despite this, a common issue that these systems run into is the creation of unhelpful rules, caused by having multiple features in the data representing similar areas of knowledge. While we can logically know that these rules will not be useful in conjunction with each other, this is much more difficult for the algorithm to innately know.