Multiple comparison procedures
Multiple comparison procedures
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary Multi-objective Ranking with Uncertainty and Noise
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Proceedings of the 25th international conference on Machine learning
Raced profiles: efficient selection of competing compiler optimizations
Proceedings of the 2009 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Automatic configuration of multi-objective ACO algorithms
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Applications of racing algorithms: an industrial perspective
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-objective model selection problems in the sense of Pareto optimality. As a racing algorithm, S-Race attempts to eliminate candidate models as soon as there is sufficient statistical evidence of their inferiority relative to other models with respect to all objectives. This approach is followed in the interest of controlling the computational effort. S-Race adopts a non-parametric sign test to identify pair-wise domination relationship between models. Meanwhile, Holm's Step-Down method is employed to control the overall family-wise error rate of simultaneous hypotheses testing during the race. Experimental results involving the selection of superior Support Vector Machine classifiers according to 2 and 3 performance criteria indicate that S-Race is an efficient and effective algorithm for automatic model selection, when compared to a brute-force, multi-objective selection approach.