Multiobjective Optimization of Classifiers by Means of 3D Convex-Hull-Based Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Zhao:2016:IS,

• author = "Jiaqi Zhao and Vitor Basto Fernandes and Licheng Jiao and Iryna Yevseyeva and Asep Maulana and Rui Li and Thomas Back and Ke Tang and Michael T. M. Emmerich",
• title = "Multiobjective Optimization of Classifiers by Means of {3D} Convex-Hull-Based Evolutionary Algorithms",
• journal = "Information Sciences",
• year = "2016",
• volume = "367-368",
• pages = "80--104",
• month = "1 " # nov,
• keywords = "genetic algorithms, genetic programming, Convex hull, Classification, Evolutionary multiobjective optimization, Parsimony, ROC analysis, Anti-spam filters",
• ISSN = "0020-0255",
• DOI = "doi:10.1016/j.ins.2016.05.026",
• URL = "http://www.sciencedirect.com/science/article/pii/S0020025516303504",
• abstract = "The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyse the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator-based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.",
• notes = "Replaces \cite{oai:arXiv.org:1412.5710}?",

}

Genetic Programming entries for Jiaqi Zhao Vitor Basto-Fernandes Licheng Jiao Iryna Yevseyeva Asep Maulana Rui Li Thomas Back Ke Tang Michael Emmerich

Citations