Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Tuned data mining: a benchmark study on different tuners
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Computational Intelligence (CI) provides good and robust working solutions for global optimization. CI is especially suited for solving difficult tasks in parameter optimization when the fitness function is noisy. Such situations and fitness landscapes frequently arise in real-world applications like Data Mining (DM). Unfortunately, parameter tuning in DM is computationally expensive and CI-based methods often require lots of function evaluations until they finally converge in good solutions. Earlier studies have shown that surrogate models can lead to a decrease of real function evaluations. However, each function evaluation remains time-consuming. In this paper we investigate if and how the fitness landscape of the parameter space changes, when only fewer observations are used for the model trainings during tuning. A representative study on seven DM tasks shows that the results are nevertheless competitive. On all these tasks, a fraction of 10-15% of the training data is sufficient. With this the computation time can be reduced by a factor of 6-10.