Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Weighted Random Forest for Classification Problems   Several studies have shown that combining machine learning models in an
appropriate way will introduce improvements in the individual predictions made
by the base models. The key to make well-performing ensemble model is in the
diversity of the base models. Of the most common solutions for introducing
diversity into the decision trees are bagging and random forest. Bagging
enhances the diversity by sampling with replacement and generating many
training data sets, while random forest adds selecting a random number of
features as well. This has made the random forest a winning candidate for many
machine learning applications. However, assuming equal weights for all base
decision trees does not seem reasonable as the randomization of sampling and
input feature selection may lead to different levels of decision-making
abilities across base decision trees. Therefore, we propose several algorithms
that intend to modify the weighting strategy of regular random forest and
consequently make better predictions. The designed weighting frameworks include
optimal weighted random forest based on ac-curacy, optimal weighted random
forest based on the area under the curve (AUC), performance-based weighted
random forest, and several stacking-based weighted random forest models. The
numerical results show that the proposed models are able to introduce
significant improvements compared to regular random forest.

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The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org