Non-Ordered Data Mining Rules Through Multi-Objective Particle Swarm Optimization: Dealing with Numeric and Discrete Attributes

Multi-objective meta-heuristics 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. This work describes a multi-objective particle swarm optimization (MOPSO) algorithm that handles with numerical and discrete attributes. The algorithm is evaluated by using the area under ROC curve and comparing the performance of the induced classifiers with other ones obtained with well known rule induction algorithms. The approximation sets produced by the algorithm are also analyzed following multi-objective methodology.