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Here we concentrate on binary decision trees and show that the best results are obtained using the Z-criterion to build the trees without pruning.
When we compare the convergence times of the simulations with partial views with Random implementation (see Figure 14) and the simulations of random subset heuristic without pruning, we do not observe any major discrepancies.
For example, when radiata pine is grown for structural timber without pruning, 80%% of the stand value falls within the first 19 m of tree height (New Zealand Forest Owners Association 2013).
In this section, we compare the runtime performance of two implementation versions of gMSV: the pruning gMSV uses the lower bound of gSide to prune the search space of subgraph enumerations, as shown in Algorithm 1; the unpruning gMSV denotes the method without pruning in the subgraph mining process, e.g., deleting the line 13 in Algorithm 1.
Now, at the risk of sounding like the Grinch of holiday decorating, I need to add here: That is not an excuse to keep piling on without pruning.
Specifically, each single tree is fully grown on a bootstrap sample of the training data, without pruning the leaves, and at each node split only a subset of the variables is considered.
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For each of these methods, we compare their DEG prediction performance with or without DB pruning.
We calculate AUC values with K up to 1000 for four DEG algorithms with or without DB pruning.
We tested four popular DEG identification algorithms on the 17 GEO datasets with or without DB pruning.
Table 6 shows how the number of samples of the data set affects the number of true DEGs predicted by current algorithms with or without DB pruning.
To obtain more intuitive understanding of how DB pruning improves current DEG algorithms, we showed in Table 5the total numbers of true DEGs out of top K predictions identified by different algorithms from the 17 datasets with or without DB pruning.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com