关键词： 加权随机森林   分类预测   microRNA   三阴性乳腺癌  

摘要： 目的基于microRNA组学数据,探讨加权随机森林在三阴性乳腺癌分类预测中的应用,为疾病诊断提供方法学支撑。方法以TCGA乳腺癌数据为例,采用加权随机森林构建三阴性乳腺癌的分类预测模型,并与随机森林、logistic回归、支持向量机、LASSO和岭回归五种模型进行比较。结果通过比较六种模型的5个评价指标,加权随机森林模型的预测性能明显优于其他五种模型,加权随机森林模型的灵敏度为0.852、特异度为0.873、准确度为0.871、AUC值为0.862和G-means值为0.861。结论加权随机森林构建的分类预测模型较好地识别了三阴性乳腺癌患者,可为三阴性乳腺癌的诊断提供方法学上的参考。

关键词： cancer type classification   ensemble method   performance-weighted-voting model   linear regression   single-nucleotide polymorphism  

摘要： Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic *** machine learning techniques can be used to help with cancer analysis and ***:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer *** ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five *** weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression *** final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted ***:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46%,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting *** addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall ***:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be *** addition,our model presents a good strategy for using ensemble systems for cancer type classification.

关键词： 自然语言处理   机器学习方法   网站   分类方法   检测精度   期望   个体   服务  

摘要： 识别所有客户的服务恢复期望对企业的服务恢复非常重要。为了解决这一问题,本文以汽车投诉网站的期望分类为根据提出了一种新的期望分类方法,并使用基于自然语言处理和机器学习方法检测新的期望分类方法中的个体期望。与原始网站期望分类相比,运用相同的方法进行个体期望检测,我们提出的方法能够得到更好的检测精度。

关键词： 机器学习   灾难生命支持课程   影响因素   Apriori关联规则分析   决策树  

摘要： 2019年底新型冠状病毒(2019-nCoV)肺炎疫情爆发,疫情使人们更加认识到应急救援队伍建设和相关人才培养的重要性。本文以2018—2019年间参加灾难生命支持课程(National Disaster Life Support,NDLS)培训的学员数据为研究对象,利用非监督和监督2种机器学习方法相结合的方式,分析NDLS培训效果的影响因素,帮助培训组织对学员的培训效果进行有效预判,从而提前干预,提高培训质量。首先运用Apriori算法找出若干个对培训效果影响较大的因素,然后用决策树模型对培训效果进行预测,并利用决策树分析的结果验证关联分析的结论。以置信度、支持度及提升度等参数作为Apriori关联规则的评价指标。用十折交叉验证作为决策树预测模型评估的方法。结果显示模型效果良好,其结果可以帮助培训组织对学员的学习效果进行有效预判、监控并保证培训质量。

关键词： SAM   基因表达数据   基因选择   分类预测  

摘要： 目的使用高斯核函数和欧式距离函数改进微阵列显著分析法(significance analysis of microarray,SAM)得到MSAM1法(modified significance analysis of microarray-1,MSAM1)和MSAM2法(modified significance analysis of microarray-2,MSAM2),与SAM法、Relief法、支持向量机递归特征消除法(support vector machine recursive feature elimination, SVM-RFE)进行对比,评价在基因表达数据中MSAM1法、MSAM2法的基因选择和分类预测能力。方法从Bioconductor中的golubEsets包获得leukemia数据集(Golub等人给出了该数据集所包含的50个差异基因),运用R软件实现5种算法,分别用正确率和ROC曲线下面积即AUC值评价基因选择能力和分类预测能力,用Kruskal-Wallis H检验比较5种方法的正确率和AUC值的组间差异,进一步的两两比较采用SNK-q检验。结果正确率和AUC值均表现为MSAM1和MSAM2最优,SAM和SVM-RFE法次之,Relief法排在最后;5种方法的组间差异有统计学意义(H=150.333,P<0.0001和H=293.2579,P<0.0001),两两比较结果显示虽然MSAM1和MSAM2之间差异无统计学意义(P>0.05),但两种方法与其他3种方法之间差异均有统计学意义(P<0.05)。结论用高斯核函数和欧式距离函数改进的加权SAM法提高了SAM法的基因选择和分类预测能力,在实际基因表达数据的应用中可以得到更为稳定的分析结果。

关键词： 黎曼流形   黎曼几何   计算机视觉   深度学习   数学基本概念   机器学习方法  

摘要： |史泽林黎曼几何在计算机视觉中的应用研究进展从数学基本概念出发,阐述黎曼流形与图像的关系以及视觉应用的可行性,并介绍计算机视觉中具有重要应用的几种黎曼流形。对黎曼流形在计算机视觉中若干常见应用进行了概述,重点介绍了与深度学习相结合的相关进展。最后,对引入黎曼流形的机器学习方法的未来发展进行了分析和讨论。

关键词： supercomputer   high performance computing   job management system   supercomputer job scheduling   machine learning   job runtime prediction   SUPPZ   random forest  

摘要： The paper is devoted tomachine learningmethods and algorithms for the supercomputer jobs execution prediction. The supercomputers statistics shows that the actual runtime of the most of the jobs substantially diverges from the time requested by the user. This reduces the efficiency of scheduling jobs, since an inaccurate job execution time estimation leads to a suboptimal jobs schedule. The job classification is considered, it is based on the difference between the job actual and the requested execution time. Forecast was made on the base of supercomputer multiuser job management system statistics by assigning a submitted job to one of the classes. The statistics of supercomputers MVS-100K and MVS-10P in the Joint Supercomputer Center of the Russian Academy of Sciences (JSCC RAS) was used. The job flow feature ranking by importance was done on the statistical analysis results. The cross-correlation of the most important features was determined. The probability estimates of correct prediction were obtained for selected wellknown machine learning algorithms: logistic regression, decision trees, k-nearest neighbors, linear discriminant analysis, support vector machine, random forest, gradient boosting, and feedforward neural network. The best values were obtained using the random forest method.

关键词： pixel-based supervised classification   random forest   support vector machine   gray level cooccurrence matrix (GLCM)   principal component analysis (PCA)   WorldView-3  

摘要： Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

关键词： Reference evapotranspiration   Deep learning neural network   Temporal convolution neural network   Long short-term memory neural network  

摘要： To evaluate the performance of deep learning methods (DL) for reference evapotranspiration estimation and to assess the applicability of the developed DL models beyond the study areas where they were trained, three popular DL models named deep neural network (DNN), temporal convolution neural network (TCN), and long short-term memory neural network (LSTM) were developed to estimate daily reference evapotranspiration (ETo) using incomplete meteorological data in the Northeast plain, China. The performances of the three DL models were compared to two classical machine learning models (CML)-support vector machine (SVM) and random forest (RF)-and empirical equations, including two temperature-based (Hargreaves (H) and modified Hargreaves (MH)), three radiation-based (Ritchie (R), Priestley-Talor (P), and Makkink (M)), and two humiditybased (Romanenko (ROM) and Schendel (S)) empirical models, in two strategies: (1) all proposed models were trained, tested, and compared in each single weather station, and (2) all-weather stations were split into several groups using the K-means method with their mean climatic characteristics. Then, in each group, stations took turns testing the proposed models which were trained by rest of the stations. The results showed that (1) the coefficient of determination (R-2) values of the TCN and RF were 0.048 and 0.035 significantly higher than that of MH, respectively, and the relative root mean error (RMSE) values of TCN and RF were substantially 0.096, and 0.074 mm/d lower than that of MH, indicating that TCN and RF performed better than empirical models in the first strategy, and TCN and LSTM exhibited an RMSE that was significantly decreased by 0.069 and 0.079 mm/d, showing that TCN and LSTM outperformed empirical models in the second strategy, compared with the MH method;(2) in both strategies, compared with the Ritchie (R) model, TCN, LSTM, DNN, RF, and SVM increased R-2 and decreased RMSE significantly, especially the TCN model;(3) sim

关键词： Bayesian model averaging   Ensemble method   Solar radiation   Wavelet   Artificial neural networks   Extreme learning machines   Radial basis function  

摘要： The present study investigated the potential of new ensemble method, Bayesian model averaging (BMA), in modeling monthly solar radiation based on climatic data. Data records covered monthly maximum temperature (T-max), minimum temperature (T-min), sunshine hours (H-s), wind speed (W-s), relative humidity (RH), and solar radiation values obtained from two weather stations of Turkey. The BMA estimates were compared with the artificial neural networks (ANN), extreme learning machines (ELM), radial basis function (RBF), and their hybrid versions with wavelet transform technique (wavelet-ANN or WANN, wavelet-ELM or WELM, and wavelet-RBF or WRBF). Three evaluation criteria e.g., root mean square error (RMSE), Nash-Sutcliffe efficiency, and determination coefficient (R-2), were applied to measure the accuracy of the employed methods. The results indicated the superior accuracy of the BMA4 models over six machine learning models for estimating monthly solar radiation;improvements in accuracy of ANN4, ELM4, RBF4, WANN4, WELM4, and WRBF4 models comprising T-max, T-min, H-s, W(s)and RH input variables were about 56-41%, 44-31%, 57-46%, 35-26%, 27-16%, and 43-28% in terms of RMSE reduction in both stations. While the hybrid models (i.e., WANN4, WELM4, and WRBF4) increased the accuracy of the single models about 31-21%, 23-18%, and 26-25% for ANN4, ELM4, and RBF4, respectively.