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"Random forests," an extension of tree regression, provide a relatively new technique for exploring relationships of a response variable like the density of indicator bacteria in water to numerous potential explanatory variables. We used this tool to study relationships of indicator density at five beaches to numerous other variables and found that day of the week, indicator density 24h earlier, water depth at the sampling point, cloud cover, and others were related to density at one or more of the beaches. Using data from the first 52 days of measurement allowed predicting indicator densities in the following 10 days to order of magnitude at some of the beaches. Our analyses served to demonstrate the potential usefulness of this analytic tool for large data sets with many variables.  相似文献   
In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Relational classifiers differ with respect to how they handle these sets: some use properties of the set as a whole (using aggregation), some refer to properties of specific individuals of the set, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This article describes a learning approach that avoids this bias, using first order random forests. Essentially, an ensemble of decision trees is constructed in which tests are first order logic queries. These queries may contain aggregate functions, the argument of which may again be a first order logic query. The introduction of aggregate functions in first order logic, as well as upgrading the forest’s uniform feature sampling procedure to the space of first order logic, generates a number of complications. We address these and propose a solution for them. The resulting first order random forest induction algorithm has been implemented and integrated in the ACE-ilProlog system, and experimentally evaluated on a variety of datasets. The results indicate that first order random forests with complex aggregates are an efficient and effective approach towards learning relational classifiers that involve aggregates over complex selections. Editor: Rui Camacho  相似文献   
无监督聚类在锂离子电池分类中的应用   总被引:1,自引:0,他引:1  
单体电池的一致性,决定了电池组的性能,如何选出性能一致的单体电池又一直是电池组研究中的重点所在。本文采集了100个合格锂离子电池的6项性能指标(老化前后电压、容量、内阻、1C放电平台、电芯厚度),运用主成分分析(PCA)、核主成分分析(KPCA)、随机森林(RF)3种无监督聚类方法,对数据结构进行了研究。结果表明,数据指标之间存在复杂的非线性关系,主成分分析和核主成分分析,均未能形成明显聚类,但随机森林数据在低维空间显然形成4类,任意从中选4个电池组成电池组作循环性能仿真测试,结果显示由由该方法挑选出的单体电池具有较好的一致性。  相似文献   
基于改进的随机森林算法的入侵检测模型   总被引:5,自引:0,他引:5       下载免费PDF全文
郭山清  高丛  姚建  谢立 《软件学报》2005,16(8):1490-1498
针对现有入侵检测算法对不同类型的攻击检测的不均衡性和对攻击的响应时间较差的问题.将随机森林算法引入到入侵检测领域,构造了基于改进的随机森林算法的入侵检测模型,并把这种算法用于基于网络连接信息的数据的攻击检测和异常发现.通过对DARPA数据的入侵检测实验,其结果表明,基于改进的随机森林算法的入侵检测模型是可行的、高效的,对数据集DARPA中所包含的4种类型的攻击检测具有良好的均衡性.  相似文献   
为解决降水预测中存在的非线性问题,避免传统人工智能方法的过拟合弊端,提高中长期降水预测精度,引入了随机森林算法,通过优选预报因子,分别构建了年、月降水预测模型,并应用南京市1951~2013年降水系列及水文气象因子系列,验证所建模型的适用性。结果表明,随机森林模型预测精度较高、稳定性好、泛化能力强,能有效预测年、月降水量;与BP神经网络模型和支持向量机模型相比,随机森林模型效率更高、性能更优,尤其适用于大样本的逐月降水量预测中。  相似文献   
针对区域水资源可持续利用评价中指标多、噪声复杂和非线性的特点以及传统方法缺乏可操作性、难以解决稳健性低和过学习等问题,介绍了一种稳健性较高的智能学习方法——随机森林,将其应用于区域水资源可持续利用评价中,并以汉中盆地平坝区为例,对该方法的评价效果进行了验证。结果表明,与SP插值、人工神经网络(ANN)和支持向量机(SVM)模型评价结果相比,本文方法实用性强、稳健性较高、泛化性能高,在分类预测阶段和交叉验证阶段分类准确率均高达100%;同时可知,在影响区域水资源可持续利用的各评价指标中,水资源利用率和人均供水量的影响较为重要。  相似文献   
State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with generic weights, which are assumed to be universally and equally good for all individuals, independent of people's different appearances. In this study, we show that certain features play more important role than others under different viewing conditions. To explore this characteristic, we propose a novel unsupervised approach to bottom-up feature importance mining on-the-fly specific to each re-identification probe target image, so features extracted from different individuals are weighted adaptively driven by their salient and inherent appearance attributes. Extensive experiments on three public datasets give insights on how feature importance can vary depending on both the viewing condition and specific person's appearance, and demonstrate that unsupervised bottom-up feature importance mining specific to each probe image can facilitate more accurate re-identification especially when it is combined with generic universal weights obtained using existing distance metric learning methods.  相似文献   
This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.  相似文献   
Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA.  相似文献   
This paper deals with the problem of estimating the human upper body orientation. We propose a framework which integrates estimation of the human upper body orientation and the human movements. Our human orientation estimator utilizes a novel approach which hierarchically employs partial least squares-based models of the gradient and texture features, coupled with the random forest classifier. The movement predictions are done by projecting detected persons into 3D coordinates and running an Unscented Kalman Filter-based tracker. The body orientation results are then fused with the movement predictions to build a more robust estimation of the human upper body orientation. We carry out comprehensive experiments and provide comparison results to show the advantages of our system over the other existing methods.  相似文献   
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