CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data.
CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data.
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DOI：
10.1371/journal.pone.0144820
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Knijnenburg TA
中科院分区：
文献类型：
--
作者：
Bressler R;Kreisberg RB;Bernard B;Niederhuber JE;Vockley JG;Shmulevich I;Knijnenburg TA
随机森林已经成为计算生物学中标准的数据分析工具。然而，为了处理生物数据集及其相关研究问题的复杂性，通常需要对现有实现进行扩展。这些不断增长的数据集需要高性能的实现。我们描述了CloudForest，一个用Go语言编写的随机森林包，它特别适合大型、异构、遗传和生物医学数据集。CloudForest包括几个扩展，比如处理不平衡类和缺失值。其灵活的设计使用户能够轻松实现额外的扩展。CloudForest通过有效地使用CPU缓存、针对不同类别的特性进行优化和高效的多线程来实现快速运行时间。https://github.com/ilyalab/CloudForest。
Random Forest has become a standard data analysis tool in computational biology. However, extensions to existing implementations are often necessary to handle the complexity of biological datasets and their associated research questions. The growing size of these datasets requires high performance implementations. We describe CloudForest, a Random Forest package written in Go, which is particularly well suited for large, heterogeneous, genetic and biomedical datasets. CloudForest includes several extensions, such as dealing with unbalanced classes and missing values. Its flexible design enables users to easily implement additional extensions. CloudForest achieves fast running times by effective use of the CPU cache, optimizing for different classes of features and efficiently multi-threading. https://github.com/ilyalab/CloudForest.