关键词： Data mining   Random forest   CART   Decision tree   Food analysis  

摘要： In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

关键词： Big data   Data integration   Farm matching   Probabilistic   Machine learning  

摘要： The advent of big data in agriculture increased the necessity of extracting useful information from large data collections. This knowledge is critical in optimizing production systems, while also addressing prevailing issues such as sustainability. One of the first, yet crucial, data analytics steps comprises integration. Integrated data from different sources can provide enhanced insight, as they may retain complementary information on the same entity. In the absence of a "unique universal identifier" to link entities (e.g. farms) from different databases, it is necessary to rely on their recorded attributes (e.g. farm name, owner). We propose a fully automated framework to match farms, across different datasets (i.e. farm matching) in a big data context. To assess performance, we used information on Brazilian beef cattle farms from two large datasets: 44,566 farms that made purchases at an animal nutrition company, and 32,776 that processed cattle at a meat packing company. Geographical search space reduction was implemented as an alternative to reduce the number of comparisons evaluated. To compare attributes between farm pairs, we contrasted two edit-based approaches, the Levenshtein and Jaro-Winkler metrics. We also compare deterministic, stochastic, and machine learning (ML) approaches, for classification of farm pairs as match or non-match. These techniques have been used in other record linkage domains. The deterministic approach requires all attributes to match exactly. The probabilistic approaches tested were Epi-Weights (CR) and Fellegi-Slunter (FS). Unsupervised ML approaches were k-means and bagged clustering (BC). Supervised methods were recursive partitioning trees, bagging of decision trees, bootstrap based classification trees, stochastic boosting, support vector machines (SVM), single-layer neural networks and logistic regression. Labels were produced by specialist review for both a training set of 295,012 comparison pairs and a testing set of 32,7

关键词： Gene prediction   Supervised learning   Markov model   Sequence encoding   Intron density  

摘要： Identification of splice sites is imperative for prediction of gene structure. Machine learning-based approaches (MLAs) have been reported to be more successful than the rule-based methods for identification of splice sites. However, the strings of alphabets should be transformed into numeric features through sequence encoding before using them as input in MLAs. In this study, we evaluated the performances of 8 different sequence encoding schemes i.e., Bayes kernel, density and sparse (DS), distribution of tri-nucleotide and 1st order Markov model (DM), frequency difference distance measure (FDDM), paired-nucleotide frequency difference between true and false sites (FDTF), 1st order Markov model (MM1), combination of both 1st and 2nd order Markov model (MM1 + MM2) and 2nd order Markov model (MM2) in respect of predicting donor and acceptor splice sites using 5 supervised learning methods (ANN, Bagging, Boosting, RF and SVM). The encoding schemes and machine learning methods were first evaluated in 4 species i.e., A. thaliana, C. elegans, D. melanogaster and H. sapiens, and then performances were validated with another four species i.e., Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum and Trypanosoma brucei. In terms of ROC (receiver-operating-characteristics) and PR (precision-recall) curves, FDTF encoding approach achieved higher accuracy followed by either MM2 or FDDM. Further, SVM was found to achieve higher accuracy (in terms of ROC and PR curves) followed by RF across encoding schemes and species. In terms of prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes. Further, splice site prediction accuracies were observed higher for the species with low intron density. To our limited knowledge, this is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned. We have also developed an R-package

关键词： 储层分类   二分类问题   机器学习   线性支持向量机  

摘要： 油气田开发中有效储层和非有效储层的样本点存在混合带时,两类储层的划分是一个难点问题.从统计学上来看,其本质是一个含噪声的小样本二分类问题,可以采用机器学习方法,充分挖掘有试油成果的样本点的数据信息.分别利用线性判别分析、支持向量机、多层感知机神经网络建立储层分类模型,利用10次10折交叉验证法进行模型评估与优选,并利用全部样本点建立了有效的储层分类模型,最后将模型推广应用到样本分布的三种不同情形.结果表明,线性支持向量机模型具有最好的分类效果和很强的泛化能力,对于区分有效储层和非有效储层是有效的,可以在油气田开发中进行推广.

关键词： Syk   虚拟筛选   分子描述符   机器学习方法   C4.5决策树   随机森林  

摘要： 脾酪氨酸激酶(Syk)是一种细胞内的酪氨酸激酶细胞质受体,在类风湿性关节炎(RA)的发病过程中起着至关重要的作用。筛选Syk抑制剂对RA的治疗有着重要的意义。采用C4.5决策树与随机森林(RF)两种机器学习方法分别对Syk抑制剂与非抑制剂建立模型,经过对比,RF具有更好的预测精度。采用RF模型对Syk抑制剂进行虚拟筛选,从ZINC数据库筛选得到潜在的Syk抑制剂分子。研究结果表明,机器学习方法对于虚拟筛选和发现潜在的Syk抑制剂十分有效。

关键词： 相关向量机(RVM)   教学质量评价   精度分析   可靠性分析   遗传算法   置信区间  

摘要： 为提高高校理论教学与实验教学质量评价的精度及可靠性,基于机器学习新算法RVM构建高校理论教学与实验教学质量评价新模型。对RVM的核函数及参数确定方法进行改进,利用遗传算法优化的MK-RVM模型分别建立高校理论与实验教学质量评价的MK-RVM新模型,并构建基于置信区间的结果可靠性分析方法。实验结果表明,高校理论教学与实验教学质量评价MK-RVM模型的各项精度指标均表现优秀,精度等级达到“好”;训练集及测试集的教学质量评价真实值均在MK-RVM估计的95%置信度置信区间内,证实基于MK-RVM的教学质量评价结果具有较好的可靠性。因此,RVM算法为高校理论教学与实验教学质量评价提供了一种新的有效途径。

关键词： Teaching methods   Annual precipitation   Artificial intelligence   Weather extremes   Daily   Precipitation   Neural networks   Climatic extremes   Climate change   Spatial discrimination   Statistical models   Statistical analysis   Machine learning   Climate effects   Disaggregation   Extreme values   Learning algorithms   Statistical modelling   Modelling   Rainfall   Support vector machines   Climate models   Statistical methods   Global climate   Learning   Global climate models   Methodology   Anomalies   Annual   Intercomparison   Mathematical models   Climatic changes  

摘要： Statistical downscaling of Global Climate Models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have not been explored compared to traditional approaches. In this paper, we compare five Perfect Prognosis (PP) approaches, Ordinary Least Squares, Elastic-Net, and Support Vector Machine along with two machine learning methods Multi-task Sparse Structure Learning (MSSL) and Autoencoder Neural Networks. In addition, we introduce a hybrid Model Output Statistics and PP approach by modeling the residuals of Bias Correction Spatial Disaggregation (BCSD) with MSSL. Metrics to evaluate each method's ability to capture daily anomalies, large-scale climate shifts, and extremes are analyzed. Generally, we find inconsistent performance between PP methods in their ability to predict daily anomalies and extremes as well as monthly and annual precipitation. However, results suggest that L-1 sparsity constraints aid in reducing error through internal feature selection. The MSSL+BCSD coupling, when compared with BCSD, improved daily, monthly, and annual predictability but decreased performance at the extremes. Hence, these results suggest that the direct application of state-of-the-art machine learning methods to statistical downscaling does not provide direct improvements over simpler, longstanding approaches.

关键词： Machine Learning   CA-125 Antigen   Ovarian Neoplasms   Prognosis   Survival  

摘要： Objectives: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method. Methods: Information of EOC patients from Samsung Medical Center (training cohort, n=1,128) was analyzed to optimize the prognostic model using GB. The performance of the final model was externally validated with patient information from Asan Medical Center (validation cohort, n=229). The area under the curve (AUC) by the GB model was compared to that of the conventional Cox proportional hazard regression analysis (CoxPHR) model. Results: In the training cohort, the AUC of the GB model for predicting second year overall survival (OS), with the highest target value, was 0.830 (95% confidence interval [CI]= 0.802-0.853). In the validation cohort, the GB model also showed high AUC of 0.843 (95% CI= 0.833-0.853). In comparison, the conventional CoxPHR method showed lower AUC (0.668 (95% CI= 0.617-0.719) for the training cohort and 0.597 (95% CI= 0.474-0.719) for the validation cohort) compared to GB. New classification according to survival probability scores of the GB model identified four distinct prognostic subgroups that showed more discriminately classified prediction than the International Federation of Gynecology and Obstetrics staging system. Conclusion: Our novel GB-guided classification accurately identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method. This approach would be useful for accurate estimation of individual outcomes of EOC patients.

摘要： Generic drug products are approved by the US Food and Drug Administration (FDA) through Abbreviated New Drug Applications (ANDAs). The ANDA review and approval involves multiple offices across the FDA. Forecasting ANDA submissions can critically inform resource allocation and workload management. In this work, we used machine learning (ML) methodologies to predict the time to first ANDA submissions referencing new chemical entities following their earliest lawful ANDA submission dates. Drug product information, regulatory factors, and pharmacoeconomic factors were used as modeling inputs. The random survival forest ML method, as well as the conventional Cox model, was used for ANDA submission predictions. The ML method outperformed the conventional Cox regression model in predictive performance that was adequately assessed by both internal and external validations. In conclusion, it can potentially serve as an effective forecasting tool for strategic workload and research planning for generic applications.

关键词： Intrusion detection system   Machine learning algorithm   Hybrid system   Feature selection   NSL-KDD  

摘要： In this study, a hybrid and layered Intrusion Detection System (IDS) is proposed that uses a combination of different machine learning and feature selection techniques to provide high performance intrusion detection in different attack types. In the developed system, firstly data preprocessing is performed on the NSL-KDD dataset, then by using different feature selection algorithms, the size of the dataset is reduced. Two new approaches have been proposed for feature selection operation. The layered architecture is created by determining appropriate machine learning algorithms according to attack type. Performance tests such as accuracy, DR, TP Rate, FP Rate, F-Measure, MCC and time of the proposed system are performed on the NSL-KDD dataset. In order to demonstrate the performance of the proposed system, it is compared with the studies in the literature and performance evaluation is done. It has been shown that the proposed system has high accuracy and a low false positive rates in all attack types.