Growing rule-based induction system

Learning classifier systems (LCSs) are rule-based systems that have widely been used in data mining over the last few years. This paper employs UCS, a supervised learning classifier system, that was a version of LCSs for classification in data mining tasks. In this paper, we propose an adaptive framework of a rule-based competitive learning environment. In this framework, a growing neural gas (GNG) is used to adaptively cluster the data instances as they arrive. Each instance is then assigned to based classifier, the UCS responsible for the corresponding cluster. Through this mechanism, the complexity of a classification problem is decomposed adaptively into subproblems, each with a lower or equal complexity to the overall problem. Since each instance is exposed to a smaller population size than the single population approach, the throughput of the system increases. The experiments show that the proposed framework can decompose a problem adaptively into several subproblems. The accuracy rate of UCS in the distributed environment can also be better than the normal environment.