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Random forests with stochastic induction of decision trees

机译：随机森林具有决策树的随机诱导

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摘要

In this paper, a novel stochastic approach for the induction of the decision trees in a tree-structured ensemble classifier is presented. The proposed algorithm is based on a stochastic process to induct each decision tree, assigning a probability for the selection of the split attribute in every tree node, designed in order to create strong and independent trees. A selection of 33 well-known classification datasets have been employed for the evaluation of the proposed algorithm, obtaining high classification results, in terms of Classification Accuracy, Average Sensitivity and Average Precision. Furthermore, a comparative study with Random Forest, Random Subspace and C4.5 is performed. The obtained results indicate the importance of the proposed algorithm, since it achieved the highest overall results in all metrics.
机译：本文介绍了一种新的随机方法，用于诱导树木结构集合分类器中决策树的诱导。所提出的算法基于一个归档每个决策树的随机过程，为每个树节点中选择拆分属性的概率，设计为创建强大和独立的树木。选择33个着名的分类数据集已用于评估所提出的算法，从分类精度，平均灵敏度和平均精度获得高分类结果。此外，进行了随机林，随机子空间和C4.5的对比研究。所获得的结果表明了该算法的重要性，因为它达到了所有度量的最高结果。

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