A new approach for multi-label classification based on default hierarchies and organizational learning

Abstract

Learning Classifier Systems (LCSs) are a class of expert systems that use a knowledge base of decision rules and a genetic algorithm (GA) [9] as a discovery mechanism. The set of decision rules allows the LCS to represent and learn control strategies, while the robust search ability of the GA allows it to search for new rules based on the performance of existing rules. LCS were first designed to solve machine learning problems, especially classification problems. Classification problems are problems where instances of a data set belong to a set of classes, and the system needs to infer, based on past experience, the correct class (or classes) of new, previously unseen, instances. However, the features of LCSs are also very useful for solving reinforcement learning problems, a class of problems where the system should learn to operate in the environment based only on performance feedback. This paper considers LCSs as an approach to classification problems, more specifically a more complex kind of classification called multi-label classification. This paper analyses the default hierarchy formation theory presented by [14] as a way of favoring the hierarchical arrangement of rules, and also the organizational learning theory [17] for adjusting the degree of individual and collective behaviors. We suggest a new method, combining both organizational learning and default hierarchy formation, for solving multi-label problems. The preliminary results with a simple multi-label problem show the potential of this method. Final discussion presents the conclusions and directions for further research.