Abstract
Multi-Objective Metaheuristics permit to conceive a complete novel approach to induce classifiers, where the properties of the rules can be expressed in different objectives, and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. Furthermore, these rules can be used as an unordered classifier, in this way, the rules are more intuitive and easier to understand because they can be interpreted independently one of the other. The quality of the learned rules is not affected during the learning process because the dataset is not modified, as in traditional rule induction approaches. With this philosophy, this chapter describes a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. One reason to choose the Particle Swarm Optimization Meta heuristic is its recognized ability to work in numerical domains. This propriety allows the described algorithm deals with both numerical and discrete attributes. The algorithm is evaluated by using the area under ROC curve and, by comparing the performance of the induced classifiers with other ones obtained with well known rule induction algorithms. The produced Pareto Front coverage of the algorithm is also analyzed following a Multi-Objective methodology. In addition to this, some application results in the Software Engineering domain are described, more specifically in the context of software testing. Software testing is a fundamental Software Engineering activity for quality assurance that is traditionally very expensive. The algorithm is used to induce rules for fault-prediction that can help to reduce testing efforts. The empirical evaluation and the comparison show the effectiveness and scalability of this new approach.
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de Carvalho, A.B., Pozo, A., Vergilio, S. (2010). A Non-ordered Rule Induction Algorithm through Multi-Objective Particle Swarm Optimization: Issues and Applications. In: Nedjah, N., dos Santos Coelho, L., de Macedo Mourelle, L. (eds) Multi-Objective Swarm Intelligent Systems. Studies in Computational Intelligence, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05165-4_2
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