The Multi-label OCS with a Genetic Algorithm for Rule Discovery: Implementation and First Results

Abstract: 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 Smit’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.

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