random forest

Numerical experiments show that the index system selected by Random Forest can effectively reflect the credit status of the enterprises, and improve the prediction accuracy of the assessment model based on Random Forest.

实验证明，该方法确定的指标体系能更有效地体现企业的信用状况，使用该指标体系建立的随机森林评估模型具有更高的预测准确率。

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The details about our works are as follows: Firstly, we built ensemble classifiers based on random forest ( RF) algorithm and support vector machine ( SVM) method to identify the RNA-binding proteins by integrating various features.

本论文的具体研究工作包括以下几个方面：1、建立了基于随机森林算法和支持向量机算法的RNA结合蛋白识别的集成算法预测模型。

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Finally, we have built a Random forest classifier on the new training sets to realize the R-gene classification.

最后在重建的训练集上，利用随机森林算法构建可以识别抗性基因的分类器。

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First step random forest algorithm is used, according to the characteristics of the evaluation to the original features of contribution, to choose the larger contribution to the classification.

第一步先用随机森林算法，依据其对特征贡献度的评价来对原始特征进行贡献度排序，选择出贡献度较大的特征。

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Since it was proposed, random forest has become a well-known data analysis method, and it has been applied to a wide variety of scientific areas.

随机森林算法自提出以来已经成为一种重要的数据分析工具，被广泛地应用于科学研究的众多领域。

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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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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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Random forest is an ensemble classification methods developed by Leo Breiman in 2001.

随机森林是Leo Breiman于2001提出的一个组合分类算法。

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The proposed algorithm is applied to time series classification experiment of silkworm and shows its advantage over normal time series random forest.

在将该算法应用在家蚕的时间序列分类问题的实验中，验证了该算法比普通时间序列随机森林的优越性。

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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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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 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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In the fourth chapter mostly introduces the basic principles of the random forest algorithm and focuses on two parts closely related with the definition of the random forest: the CART and the Bagging method at first.

第四章主要介绍了本文所采用的随机森林算法的基本原理，着重阐述与随机森林定义紧密相关的两种方法：分类回归树（CRAT）与Bagging方法。

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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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Original feature contribution is sorted by random forest algorithm which accord to the classification and recognition result, to choose the important features.

用随机森林算法对原始特征按照分类识别贡献度排序，提取重要特征。

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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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Meanwhile, as a classifier, random forest can ensure high accuracy and quick classification, and can be applied parallel computing technology which makes the speed faster.

而随机森林作为分类器能够保证高准确度和快速分类，同时可以应用并行计算技术进一步提速。

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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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As an efficient and stable algorithm for high-dimensional spectral data classification, random forest has some advantages compared to other algorithms in the efficiency and accuracy.

再次，对于高维光谱数据分类研究。随机森林是一种高效、稳定的算法，和其他算法相比在效率和准确率上具有一定的优势。

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And will describe a real-time body parts recognition algorithm which is based on self-made depth image sample library, local gradient feature extraction and combined with random forest learning.

本文主要讨论利用深度图像进行人体部位识别的问题，将阐述一种利用自制的深度图像样本库，提取局域梯度特征，结合随机森林学习的实时人体部位识别算法。

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Compared with normal time series random forest, the proposed algorithm considers both the distinguish capability and the variation property of the time series.

该算法和普通的时间序列随机森林相比，在选择决策树结点分划特征时，同时考虑了特征的区分能力及特征的时间序列变化规律特点。

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It introduces an outliers detection method based on random forest.

提出一种基于随机森林方法的异常样本（outliers）检测方法。

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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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The predicted errors of preserved 300 records were calculated by random forest, stochastic gradient boosting, support vector and artificial neuron network. The results show that artificial neuron network predict the most precisely.

并用随机森林、随机梯度Boosting、支持向量机和人工神经网络四种算法对预留的数据进行了预测，结果表明人工神经网络的预测误差最小。

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Accordingly, the incremental random forest is applied to visual tracking as core learning algorithm under particle filter framework. The classifier is updated by sequentially arrived samples during tracking process.

本文将上述增量随机森林作为鉴别型表观模型应用于视觉目标跟踪，在跟踪过程中提取新的正/负样本不断更新分类器。

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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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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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Stratified random sampling of forest survey

森林分层随机抽样调查

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Although the random forest manifests the highest recognition rate ( 100%) in a variety of classifiers, it will be decreased if any speech data into a training and a test file.

尽管随机森林在多种分类器中的识别率是最高（100%），但把任一个语音数据分成一个训练文件和一个测试文件时，其识别率也会明显下降。

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

本文基于随机森林模型，针对其在代谢组学数据分析中的应用，进行了大量的研究。

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