Authors:Simon Reichhuber andSven Tomforde

Affiliation:Intelligent Systems, Kiel University, Hermann-Rodewald-Str. 3, 24118 Kiel, Germany

Keyword(s):Evolutionary Algorithms, Bet-Based Learning, Model Selection, Gradient-Free Optimisation.

Abstract:This paper presents a framework for the application of an external bet-based evolutionary algorithm to the problem of model selection. In particular, we have defined two new risk functions, called sample space exoticness and configuration space exoticness. The latter is used to manage the risk of bet placement. Further, we explain how to implement the bet-based approach for model selection in the domain of multi-class classification and experimentally compare the performance of the algorithm to reference derivative-free hyperparameter optimisers (GA and Bayesian Optimisation) on MNIST. Finally, we experimentally show that for the classifiers SVM, MLP, and Nearest Neighbors the balanced accuracy can be increased by up to three percentage points.