Joint optimization of predictive performance and selection stability
(2020) 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — Location: Bruges, Belgium (2.October.2020)
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- Abstract
- Current feature selection methods, especially applied to high dimensional data, tend to suffer from instability since marginal modifications in the data may result in largely distinct selected feature sets. Such instability strongly limits a sound interpretation of the selected variables by domain experts. We address this issue by optimizing jointly the predictive accuracy and selection stability and by deriving Pareto-optimal trajectories. Our approach extends the Recursive Feature Elimination algorithm by enforcing the selection of some features based on a stable, univariate criterion. Experiments conducted on several high dimensional microarray datasets illustrate that large stability gains are obtained with no significant drop of accuracy.
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Citations
Hamer, V., & Dupont, P. (2020). Joint optimization of predictive performance and selection stability. ESANN 2020 - Proceedings, 1(1), 381-386. https://hdl.handle.net/2078.5/253993 (Original work published 2020)