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- Boström, Henrik, et al.
(author)
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Utilizing Information on Uncertainty for In Silico Modeling using Random Forests
- 2009
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In: Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009). - Skövde : University of Skövde. - 9789197851329 ; , s. 59-62
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Conference paper (peer-reviewed)abstract
- Information on uncertainty of measurements or estimates of molecular properties are rarely utilized by in silico predictive models. In this study, different approaches to handling uncertain numerical features are explored when using the stateof- the-art random forest algorithm for generating predictive models. Two main approaches are considered: i) sampling from probability distributions prior to tree generation, which does not require any change to the underlying tree learning algorithm, and ii) adjusting the algorithm to allow for handling probability distributions, similar to how missing values typically are handled, i.e., partitions may include fractions of examples. An experiment with six datasets concerning the prediction of various chemical properties is presented, where 95% confidence intervals are included for one of the 92 numerical features. In total, five approaches to handling uncertain numeric features are compared: ignoring the uncertainty, sampling from distributions that are assumed to be uniform and normal respectively, and adjusting tree learning to handle probability distributions that are assumed to be uniform and normal respectively. The experimental results show that all approaches that utilize information on uncertainty indeed outperform the single approach ignoring this, both with respect to accuracy and area under ROC curve. A decomposition of the squared error of the constituent classification trees shows that the highest variance is obtained by ignoring the information on uncertainty, but that this also results in the highest mean squared error of the constituent trees.
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