Reducing complexity of rule based models via meta mining

Complexity, or in other words compactness, of models generated by rule learners is one of often neglected issues, although it has a profound effect on the success of any project that utilizes the rules. Researchers strive to propose learners that are characterized by excellent accuracy, and sometimes also low computational complexity, but the size of the data model generated by the learners is often not even reported. While the model size can be disregarded from the research point of view, it is very important from the end user´s perspective. Quite often the generated model is too complex to be manually analyzed or inspected, which prohibits from using it in a real-world setting. To jill this gap, the paper proposes a novel framework, which is designed to address problem of complexity reduction of rule based models. The framework is based on a Meta Mining concept, and can be applied to enhance several of existing rule learners. Its main goal is to reduce complexity, in terms of reducing size and number of generated rules, without sacrificing accuracy of the rules. The paper proposes the framework, and tests it on a set of benchmark datasets using two well known rule learners: C5.0 and DataSqueezer. The results are encouraging, and show that 50% complexity reduction can be achieved virtually without any loss of accuracy.