An online evolutionary rule learning algorithm with incremental attribute discretization

Classification rule induction involves two main processes: finding the optimal conjuncts (attribute intervals or attribute-value pairs) and their combination (disjuncts or rules) to classify different concepts in the data. The evolutionary rule learning approaches employ an evolutionary algorithm, such as a genetic algorithm, to perform both these search operations simultaneously. This approach often leads to significant problems including population bloating and stalled evolutionary search in real-valued attribute problems, especially with higher dimensions. In this paper, we present an online evolutionary rule learning approach referred to as ERL-AID that decouples the above search processes and employs a discretization algorithm that works on the attribute space and a genetic algorithm to combine the discretized attributes into appropriate classification rules. ERL-AID applies a sliding window approach to process inputs in an online fashion. The proposed system is able to produce compact rule sets with competitive performance and could scale to higher dimensions. The experimental results show the competitiveness of our algorithm.