Reinforcement learning with classifier systems: adaptive default hierarchy formation
Applied Artificial Intelligence - Special issue: design for high autonomy
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inference for the Generalization Error
Machine Learning
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
A new ant colony algorithm for multi-label classification with applications in bioinfomatics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Voting based learning classifier system for multi-label classification
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Incorporating label dependency into the binary relevance framework for multi-label classification
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Learning Classifier Systems (LCSs) are rule-based systems that can be manipulated by a genetic algorithm. LCSs were first designed by Holland to solve classification problems and a lot of effort has been made since then, resulting in a broad number of different algorithms. One of these is called Organizational Classifier System (OCS), a LCSs that tries to organize its rule set favoring good rules to be together in the same organization. However, the proposal of OCS did not include the discovery mechanism. Recently, the OCS was applied to multi-label classification, a type of classification where one instance can have more than one associated label. The authors represented the multi-label classification problem as a default hierarchy and combined the organizational capabilities of OCS together with Smith's default hierarchy formation theory to solve a simple multi-label problem. The purpose of this paper is to extend this idea with the inclusion of a genetic algorithm for the discovery of new rules and present some initial results obtained using the new method. The preliminary results obtained show that the method is comparable to other multi-label techniques. Final discussions present the conclusions of the work and some directions for further research.