Multiobjective Optimization and Rule Learning: Subselection Algorithm or Meta-heuristic Algorithm? Export

@incollection{citeulike:3408050, abstract = {A previous work explores a Multi-Objective Subset Selection algorithm, denominated the Pareto Front Elite, to induce classifiers. These classifiers are composed by a set of rules selected following Pareto dominance concepts and forming unordered classifiers. These rules are previously created by an association rule algorithm. The performance of the classifiers induced were compared with other well known rule induction algorithms using the area under the ROC curve. The area under the ROC curve (AUC) is considered a relevant criterion to deal with imbalanced data, misclassification costs and noisy data. The results show that the Pareto Front Elite algorithm is comparable to the best known techniques. In this paper we explore multi-objective meta-heuristic approach to create rules and to build the Pareto Front using the sensitivity and specificity criteria, the chosen Metaheuristic is a Greedy Randomized Adaptive Search Procedure (GRASP) with path-relinking. We perform an experimental study to compare the two algorithms: one based on a complete set of rules, and the other based on Metaheuristic Approach. In this study we analyze the classification results, through the AUC criterion, and the Pareto Front coverage produced by each algorithm.}, author = {Ishida, Celso and Pozo, Aurora and Goldbarg, Elizabeth and Goldbarg, Marco}, citeulike-article-id = {3408050}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-88045-5_3}, doi = {10.1007/978-3-540-88045-5_3}, journal = {Innovative Applications in Data Mining}, keywords = {classification, grasp, machine-learning, metaheuristic, multi-objective, path-relinking}, pages = {47--70}, posted-at = {2008-10-15 10:30:16}, priority = {2}, title = {Multiobjective Optimization and Rule Learning: Subselection Algorithm or Meta-heuristic Algorithm?}, url = {http://dx.doi.org/10.1007/978-3-540-88045-5_3}, year = {2009} }

TY - CHAP ID - citeulike:3408050 L3 - citeulike-article-id:3408050 N2 - A previous work explores a Multi-Objective Subset Selection algorithm, denominated the Pareto Front Elite, to induce classifiers. These classifiers are composed by a set of rules selected following Pareto dominance concepts and forming unordered classifiers. These rules are previously created by an association rule algorithm. The performance of the classifiers induced were compared with other well known rule induction algorithms using the area under the ROC curve. The area under the ROC curve (AUC) is considered a relevant criterion to deal with imbalanced data, misclassification costs and noisy data. The results show that the Pareto Front Elite algorithm is comparable to the best known techniques. In this paper we explore multi-objective meta-heuristic approach to create rules and to build the Pareto Front using the sensitivity and specificity criteria, the chosen Metaheuristic is a Greedy Randomized Adaptive Search Procedure (GRASP) with path-relinking. We perform an experimental study to compare the two algorithms: one based on a complete set of rules, and the other based on Metaheuristic Approach. In this study we analyze the classification results, through the AUC criterion, and the Pareto Front coverage produced by each algorithm. JF - Innovative Applications in Data Mining EP - 70 TI - Multiobjective Optimization and Rule Learning: Subselection Algorithm or Meta-heuristic Algorithm? SP - 47 KW - classification KW - grasp KW - machine-learning KW - metaheuristic KW - multi-objective KW - path-relinking AU - Ishida, Celso AU - Pozo, Aurora AU - Goldbarg, Elizabeth AU - Goldbarg, Marco PY - 2009/// UR - http://dx.doi.org/10.1007/978-3-540-88045-5_3 ER -