Design and analysis of the NIPS2003 challenge

Abstract

We organized in 2003 a benchmark of feature selection methods, whose results are summarized and analyzed in this chapter. The top ranking entrants of the competition describe their methods and results in more detail in the following chapters. We provided participants with five datasets from different application domains and called for classification results using a minimal number of features. Participants were asked to make on-line submissions on two test sets: a validation set and a "final" test set, with performance on the validation set being presented immediately to the participant and performance on the final test set presented at the end of the competition. The competition took place over a period of 13 weeks and attracted 78 research groups. In total 1863 entries were made on the validation sets during the development period and 135 entries on all test sets for the final competition. The winners used a combination of Bayesian neural networks with ARD priors and Dirichlet diffusion trees. Other top entries used a variety of methods for feature selection, which combined filters and/or wrapper or embedded methods using Random Forests, kernel methods, neural networks as classification engine. The classification engines most often used after feature selection are regularized kernel methods, including SVMs. The results of the benchmark (including the predictions made by the participants and the features they selected) and the scoring software are publicly available. The benchmark is available at http://www.nipsfsc.ecs.soton. ac.uk/ for post-challenge submissions to stimulate further research. © Springer-Verlag Berlin Heidelberg 2006.