关键词： Aquatic species   Machine learning   Survival analysis   Ecological features   Planned intervention  

摘要： In the work, we evaluate the performance of machine learning approaches for predicting successful eradication of aquatic invasive species (AIS) and assess the extent to which eradication of an invasive species depends on the certain specified ecological features of the target ecosystem and/or features that characterize the planned intervention. We studied the outcomes of 143 planned attempts for eradicating AIS, where each attempt was described by ecological and eradication-strategy-related features of the target ecosystem. We considered several machine learning approaches to determine whether one could produce a classifier that accurately predicts weather an invasive species will be eradicated. To assess each learner's performance, we examined its tenfold cross-validated prediction accuracy as well as the false positive rate, the F-measure, and the Area Under the ROC Curve. We also used Kaplan-Meier survival analysis to determine which features are relevant to predicting the time required for each eradication program. Across the five typical machine learning approaches, our analysis suggests that learners trained by the decision tree work well, and have the best performance. In particular, by examining the trained decision tree model, we found that if an occupied area was not large and/or containments of AIS dispersal were employed, the eradication of AIS was likely to be successful. We also trained decision tree models over only the ecological features and found that their performances were comparable with that of models trained using all features. As our trained decision tree models are accurate, decision makers can use them to estimate the result of the proposed actions before they commit to which specific strategy should be applied.

关键词： 学业风险   相关性分析   关联分析   课程分类   成绩预测  

摘要： 针对大学生存在学业风险、高校管理难度增大的问题,提出了基于一卡通数据与课程分类预测学生是否存在及格风险的方法。首先对计算机学院学生的一卡通数据与课程成绩进行预处理与特征提取,利用皮尔逊相关系数与Apriori算法分析不同学期课程成绩间、早餐次数与成绩间的相关性和关联性。然后结合早餐次数与同类型课程的成绩,运用多种分类器预测学生未来成绩是否及格。结果表明,该方法可预测学生某门课程是否存在不及格风险,便于教辅人员及时干预学业困难学生。

关键词： Thermal sensation   Thermal comfort   Bagging   Machine learning   Ensemble machine learning  

摘要： In this paper, an intelligent ensemble machine learning (EML) method - Bagging was developed for thermal perception prediction. Field data was collected in naturally ventilated (NV) and split air-conditioning (SAC) dormitory buildings in hot summer and cold winter (HSCW) area of China during the summer of 2016. The indoor physical measurement and subjective survey were conducted simultaneously during the field study. To determine the merit of the proposed Bagging approach, the performances of Bagging approach were compared against the artificial neural network (ANN) and support vector machine (SVM) regarding conventional statistical indicators, i.e., mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R-2) and Pearson correlation coefficient (r). Several thermal indices, i.e., thermal sensation (TS), effective temperature (ET*), standard effective temperature (SET*) and predicted mean vote (PMV), were adopted to access the occupants' thermal comfort and evaluate the model prediction. In our case study, the Bagging model's R 2 for TS, PMV, ET degrees and SET* were 0.4986, 0.9892, 0.9920 and 0.9900, respectively. It shows higher accuracy than SVM and ANN models in thermal perception prediction and outperforms the classical PMV index in TS prediction. Results indicate the proposed Bagging model's prediction performance is reliable and is highly accurate to predict the thermal perception. (C) 2018 Elsevier B.V. All rights reserved.

关键词： 机器学习   出行方式选择   多项Logit模型   中小城市   预测精度  

摘要： 在交通出行预测中,多项Logit模型（MNL）的应用十分广泛,但是同时也存在很大的缺陷。模型假定各选择枝不可观测的随机效用部分相互独立且服从二重指数分布,但是这在很多情况下都与实际不符。本文采用机器学习方法中的决策树（Decision Tree）和支持向量机（SVM）模型与多项Logit模型进行对比分析,找出对于居民出行方式选择预测最准确的模型。本文采用的数据来源于2016年12月安顺市的居民出行调查数据,通过三个模型对比可得,SVM模型的预测精度最高,其次是决策树模型,MNL模型的预测精度相对较低。

关键词： 阿尔茨海默病   磁共振成像   人工智能   诊断  

摘要： 阿尔茨海默病（Alzheimer′s Disease,AD）是一种以认知和智力损害、行为能力下降为特点的神经退行性疾病,目前其确切病因不明,并缺少有效的治疗方案（[1]）。据估计,到2050年,AD的全球发病率将达1/85（[2]）。轻度认知功能障碍（mild cognitive impairment,MCI）是AD与健康老化的中间阶段,但并不是所有的MCI患者都会转化成AD.

关键词： PCI术预后   MACE   机器学习   非均衡分类问题  

摘要： 目的:通过对比分析六种典型机器学习方法构建的PCI术后12个月MACE(主要不良心血管事件)风险预测模型,获得最优MACE风险预测模型。方法:利用随机抽样和SMOTE方法进行样本均衡化处理,再分别利用CART回归树、C4.5条件树、Ada Boost、Bagging、Random Forest(RF)和Na?ve Bayes六种机器学习方法构建MACE风险预测模型。结果:SMOTE方法处理后的训练集可以提高预测模型的预测能力。利用RF算法构建的模型预测精度达到88.7%,预测能力明显优于其他模型。结论:利用RF算法构建的PCI术后发生MACE的风险预测模型,可有效帮助医护人员对患者提早进行干预指导,降低患者不良事件的发生。

关键词： Multi-criteria decision strategy   analytical hierarchy process   indoor positioning   fingerprinting   machine learning  

摘要： Indoor positioning system is an active research area. There are various performance metrics such as accuracy, computation time, precision, and f-score in machine learning based indoor positioning systems. The aim of this study is to present a multi-criteria decision strategy to determine suitable machine learning methods for a specific indoor positioning system. This helps to evaluate the performance of machine learning algorithms considering multiple criteria. During the experiments, UJllndoorLoc, KIOS and RFKON datasets are used from the positioning literature. The algorithms such as k-nearest neighbor, support vector machine, decision tree, naive bayes and bayesian networks are compared using these datasets. In addition to these, ensemble learning algorithms, namely adaboost and bagging, are utilized to improve the performance of these classifiers. As a conclusion, the test results for any specific dataset are reevaluated using the performance metrics such as accuracy, f-score and computation time, and a multi-criteria decision strategy is proposed to find the most convenient algorithm. The analytical hierarchy process is used for multi-criteria decision. To the best of our knowledge, this is the first work to select the proper machine learning algorithm for an indoor positioning system using multi-criteria decision strategy.

关键词： Cancer prediction   Cancer recurrence   Cancer relapse   Data mining   Machine learning   Electronic health records  

摘要： Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially curative treatments are performed. Predictive models can be built using historical patient data to model the characteristics of patients that developed cancer or relapsed. These models can then be deployed into clinical settings to determine if new patients are at high risk for cancer development or recurrence. For large-scale predictive models to be built, structured data must be captured for a wide range of diverse patients. This paper explores current methods for building cancer risk models using structured clinical patient data. Trends in statistical and machine learning techniques are explored, and gaps are identified for future research. The field of cancer risk prediction is a high-impact one, and research must continue for these models to be embraced for clinical decision support of both practitioners and patients.

关键词： Perovskite materials   Curie temperature   Machine learning   Support vector machine   Relevance vector machine   Random forest  

摘要： Curie temperature (Tc), the second order phase transition temperature, is also one of the important physical properties of perovskite materials. It is a meaningful work to quickly and efficiently predict Tc of new perovskite materials before doing a considerable amount of experimental work. In the work, SVM (support vector machine), RVM (relevance vector machine) and RF (random forest) were employed to establish the prediction models of Tc with the physicochemical parameters, respectively. The results reveal that the three models all have high precision and reliability. According to K-fold cross validation, the SVR model had better prediction performance than the RVM and RF models. Meanwhile, the potential perovskite material with higher Tc was found by using the SVR model integrated with the search strategy of genetic algorithm from the virtual samples. The methods outlined here can provide valuable hints into the exploration of materials with desired property and can accelerate the process of materials design.

关键词： machine learning methods   St. Venant equations   rating curve method   nonlinear Muskingum model   hydrograph predictions  

摘要： Machine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology started in the 1990s and have since been extensively employed. Flood hydrograph prediction is important in hydrology and is generally done using linear or nonlinear Muskingum (NLM) methods or the numerical solutions of St. Venant (SV) flow equations or their simplified forms. However, soft computing methods are also utilized. This study discusses the application of the artificial neural network (ANN), the genetic algorithm (GA), the ant colony optimization (ACO), and the particle swarm optimization (PSO) methods for flood hydrograph predictions. Flow field data recorded on an equipped reach of Tiber River, central Italy, are used for training the ANN and to find the optimal values of the parameters of the rating curve method (RCM) by the GA, ACO, and PSO methods. Real hydrographs are satisfactorily predicted by the methods with an error in peak discharge and time to peak not exceeding, on average, 4% and 1%, respectively. In addition, the parameters of the Nonlinear Muskingum Model (NMM) are optimized by the same methods for flood routing in an artificial channel. Flood hydrographs generated by the NMM are compared against those obtained by the numerical solutions of the St. Venant equations. Results reveal that the machine learning models (ANN, GA, ACO, and PSO) are powerful tools and can be gainfully employed for flood hydrograph prediction. They use less and easily measurable data and have no significant parameter estimation problem.