In this paper, it does a lot of researches on random forest when used to analyze metabolomics data.

A new time series random forest algorithm combined with regular change measurement of the time series is proposed.

本文的另一内容是对代谢组学时间序列色谱数据及时间序列随机森林分类算法进行研究，给出了一种与时间序列规律性变化度量相结合的时间序列随机森林算法。

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In the field of machine learning, random forest is an important and common method of data mining.

在机器学习领域，随机森林是一种重要和常见的数据挖掘方法。

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Random Forest Similarity-based Intrusion Detection

基于随机森林计算相似性的入侵检测算法

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At last, the prediction models were developed using support vector machine and random forest methods.

最后，将选取的描述符输入支持向量机以及随机森林算法建立模型。

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Objective We investigate the use of random forests for classification of gene expression data.

目的探讨随机森林算法在基因表达数据分类研究中的应用。

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Multi-spectral Magnetic Resonance Image Segmentation Using Random Forests

基于随机森林的多谱磁共振图像分割

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Random Forest and Its Application in Chromatographic Fingerprints

随机森林及其在色谱指纹中的应用研究

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Conclusion Random Forests possesses excellent performance in the classification of gene expression data.

结论随机森林算法对基因表达数据的分类研究有较好的判别效果。

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Multi View Text Categorization Based on Random Forests

一种基于随机森林的多视角文本分类方法

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Fault diagnosis method based on modified random forests

基于改进随机森林的故障诊断方法研究

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And random forest is chosen as classifier because of its high generalization ability.

由于随机森林参数较少，泛化能力较好，因此被选作分类器进行学习和预测。

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Automatic text classification model based on random forest

基于随机森林的文本分类模型研究

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Research methods include random forest algorithm and genetic algorithm etc.

研究的方法主要包括随机森林算法及遗传算法等。

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Setting of class weights in random forest for small-sample data

随机森林针对小样本数据类权重设置

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Random Forests Algorithm with Feature Grouping in Network Intrusion Detection

网络入侵检测中属性分组的随机森林算法

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Secondly we construct evaluation model based on random forest.

其次利用随机森林建立基金评级模型；

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The Application of Random Forests for the Classification of Gene Expression Data

基因表达数据判别分析的随机森林方法

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Random forests: an important feature genes selection method of tumor

随机森林：一种重要的肿瘤特征基因选择法

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Accurate localization of facial feature points based on random forest classifier

基于随机森林的人脸关键点精确定位方法

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We use SVM and Random method to select variables that show essential difference among five different kinds of samples in our research. The effectiveness of our method is validated by performance of classification models and assistant analysis of PCA and PLS-DA.

并分别采用随机森林和支持向量机作为数据处理的基本技术筛选反映5类样本本质差异的特征变量，通过分类模型的性能以及PCA和PLS-DA的辅助分析，证明了该方法的有效性。

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