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摘要翻译：
本文研究了随机森林分类器在所有相关特征选择问题中的应用。为此，我们首先研究了两个最近提出的所有相关的特征选择算法，它们都是随机森林包装器，在一系列大小不同的合成数据集上。我们证明了可以达到合理的预测精度，并且为处理所有相关问题而设计的启发式算法具有接近参考理想算法的性能。然后，我们将其中的一种算法应用于四类半合成数据集，以评估特定数据集的性质对特征选择结果的影响。最后，我们用一个已知的基因表达数据集对该程序进行了测试。几乎所有已确定的重要基因的相关性都得到了证实，而且还发现了几个新基因的相关性。
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英文标题：
《The All Relevant Feature Selection using Random Forest》
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作者：
Miron B. Kursa and Witold R. Rudnicki
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
In this paper we examine the application of the random forest classifier for the all relevant feature selection problem. To this end we first examine two recently proposed all relevant feature selection algorithms, both being a random forest wrappers, on a series of synthetic data sets with varying size. We show that reasonable accuracy of predictions can be achieved and that heuristic algorithms that were designed to handle the all relevant problem, have performance that is close to that of the reference ideal algorithm. Then, we apply one of the algorithms to four families of semi-synthetic data sets to assess how the properties of particular data set influence results of feature selection. Finally we test the procedure using a well-known gene expression data set. The relevance of nearly all previously established important genes was confirmed, moreover the relevance of several new ones is discovered.
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PDF链接：
https://arxiv.org/pdf/1106.5112
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