Proceedings of the seventh international conference (1990) on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Reinforcement learning with classifier systems: adaptive default hierarchy formation
Applied Artificial Intelligence - Special issue: design for high autonomy
A new ant colony algorithm for multi-label classification with applications in bioinfomatics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A niching algorithm to learn discriminant functions with multi-label patterns
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Voting based learning classifier system for multi-label classification
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
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.