Boosted PRIM with application to searching for oncogenic pathway of lung cancer
Boosted PRIM with application to searching for oncogenic pathway of lung cancer
复制标题
Boost PRIM 应用于寻找肺癌致癌途径
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
R. Tibshirani
中科院分区：
文献类型：
--
作者：
Pei Wang;Young Kim;J. Pollack;R. Tibshirani
Boosted PRIM（patient rule induction method）是一种新的两类分类算法。PRIM是这些基于树的方法的变体，在特征空间中寻找盒形区域来分离不同的类。Boosted PRIM是在Adaboost中实现PRIM风格的弱学习器，Adaboost是最流行的boosting算法之一。此外，我们通过在提升过程中引入正则化来提高算法的性能，这支持Jerry Friedman将提升视为最速下降数值优化的观点。推进PRIM的动机是解决基于阵列CGH（比较基因组杂交）数据的“寻找致癌通路”的问题，尽管该算法本身适用于一般分类问题。我们通过一些模拟研究以及肺癌阵列CGH数据集上的应用说明了该方法的性能。
Boosted PRIM (patient rule induction method) is a new algorithm developed for two-class classification problems. PRIM is a variation of those tree-based methods, seeking box-shaped regions in the feature space to separate different classes. Boosted PRIM is to implement PRIM-styled weak learners in Adaboost, one of the most popular boosting algorithms. In addition, we improve the performance of the algorithm by introducing a regularization to the boosting process, which supports the perspective of viewing boosting as a steepest-descent numerical optimization by Jerry Friedman. The motivation for boosted PRIM is to solve the problem of "searching for oncogenic pathways" based on array-CGH (comparative genomic hybridization) data, though the algorithm itself is suitable for general classification problems. We illustrate the performance of the method through some simulation studies as well as an application on a lung cancer array-CGH data set.