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.2013 Dec 1;6(6):496-505.
doi: 10.1002/sam.11196.

A Weighted Random Forests Approach to Improve Predictive Performance

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A Weighted Random Forests Approach to Improve Predictive Performance

Stacey J Winham et al. Stat Anal Data Min..

Abstract

Identifying genetic variants associated with complex disease in high-dimensional data is a challenging problem, and complicated etiologies such as gene-gene interactions are often ignored in analyses. The data-mining method Random Forests (RF) can handle high-dimensions; however, in high-dimensional data, RF is not an effective filter for identifying risk factors associated with the disease trait via complex genetic models such as gene-gene interactions without strong marginal components. Here we propose an extension called Weighted Random Forests (wRF), which incorporates tree-level weights to emphasize more accurate trees in prediction and calculation of variable importance. We demonstrate through simulation and application to data from a genetic study of addiction that wRF can outperform RF in high-dimensional data, although the improvements are modest and limited to situations with effect sizes that are larger than what is realistic in genetics of complex disease. Thus, the current implementation of wRF is unlikely to improve detection of relevant predictors in high-dimensional genetic data, but may be applicable in other situations where larger effect sizes are anticipated.

Keywords: Random Forests; gene-gene interactions; genetic data; genome wide association; high-dimensional data; weighting.

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Figures

Figure 1
Figure 1
Simulation Results (wPE and wAUC), m=2 causal pairs. Weighted Prediction Error (top row) and AUC (bottom row) plotted against total heritability for models with 2 causal interacting pairs. Results are only displayed for a subset of weights and for H2M/H2I = 0.5, 2.0; for the full results, see Supporting Information. The results for weights that are not plotted are similar and in the same range.
Figure 2
Figure 2
Simulation Results (wPE and wAUC), m=10 causal pairs. Weighted Prediction Error (top row) and AUC (bottom row) plotted against total heritability for models with 10 causal interacting pairs. Results are only displayed for a subset of weights and for H2M/H2I = 0.5, 2.0; for the full results, see Supporting Information. The results for weights that are not plotted are similar and in the same range.
Figure 3
Figure 3
Variable importance results from real data analysis of the NMDA-dependent AMPA trafficking cascade pathway. Importance is plotted for each SNP by chromosomal location for A: traditional RF using OOB estimates, and wRF with cross-validation and weights of B: x=1, C: x=(1/tPE)2, D: x=(1/tPE)5, and E: x=rank(1/tPE). Colors indicate gene membership, as specified on the x-axis. Only SNPs with importance > 0 are plotted. Importance is plotted for each SNP by chromosomal location for A: traditional RF using OOB estimates, and wRF with a single data split and weights of B: x=1, C: x=(1/tPE)2, D: x=(1/tPE)5, and E: x=rank(1/tPE). Colors indicate gene membership, as specified on the x-axis. Only SNPs with importance > 0 are plotted.
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