Multiobjective genetic programming for maximizing ROC performance

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Wang:2013:Neurocomputing,

• author = "Pu Wang and Ke Tang and Thomas Weise and E. P. K. Tsang and Xin Yao",
• title = "Multiobjective genetic programming for maximizing {ROC} performance",
• journal = "Neurocomputing",
• year = "2014",
• volume = "125",
• pages = "102--118",
• keywords = "genetic algorithms, genetic programming, Classification, ROC analysis, AUC, ROCCH, Evolutionary multiobjective algorithm, Memetic algorithm, Decision tree",
• ISSN = "0925-2312",
• URL = "http://home.ustc.edu.cn/~wuyou308/doc/mogp.pdf",
• DOI = "doi:10.1016/j.neucom.2012.06.054",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231213001938",
• size = "17 pages",
• abstract = "In binary classification problems, receiver operating characteristic (ROC) graphs are commonly used for visualising, organising and selecting classifiers based on their performances. An important issue in the ROC literature is to obtain the ROC convex hull (ROCCH) that covers potentially optima for a given set of classifiers [1]. Maximising the ROCCH means to maximise the true positive rate (tpr) and minimise the false positive rate (fpr) for every classifier in ROC space, while tpr and fpr are conflicting with each other. In this paper, we propose multiobjective genetic programming (MOGP) to obtain a group of nondominated classifiers, with which the maximum ROCCH can be achieved. Four different multiobjective frameworks, including Nondominated Sorting Genetic Algorithm II (NSGA-II), Multiobjective Evolutionary Algorithms Based on Decomposition (MOEA/D), Multiobjective selection based on dominated hypervolume (SMS-EMOA), and Approximation-Guided Evolutionary Multi-Objective (AG-EMOA) are adopted into GP, because all of them are successfully applied into many problems and have their own characters. To improve the performance of each individual in GP, we further propose a memetic approach into GP by defining two local search strategies specifically designed for classification problems. Experimental results based on 27 well-known UCI data sets show that MOGP performs significantly better than single objective algorithms such as FGP, GGP, EGP, and MGP, and other traditional machine learning algorithms such as C4.5, Naive Bayes, and PRIE. The experiments also demonstrate the efficacy of the local search operator in the MOGP framework.",
• notes = "Selected papers from the 9th International Symposium of Neural Networks, July 2012. Advances in Neural Network Research and Applications. Advances in Bio-Inspired Computing: Techniques and Applications",

}

Genetic Programming entries for Pu Wang Ke Tang Thomas Weise Edward P K Tsang Xin Yao

Citations