关键词： Causal inference   positivity   doubly robust   IPTW   TMLE   super learner  

摘要： Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produce a separation of the two exposure groups in terms of propensity score densities which can lead to biased estimates of the treatment effect. To motivate the problem, we evaluated the Targeted Minimum Loss-based Estimation procedure using a simulation scenario to estimate the average treatment effect. We highlight the divergence in estimates obtained when using parametric and data-adaptive methods to estimate the propensity score. We then adapted an existing diagnostic tool based on a bootstrap resampling of the subjects and simulation of the outcome data in order to show that the estimation using data-adaptive methods for the propensity score in this study may lead to large bias and poor coverage. The adapted bootstrap procedure is able to identify this instability and can be used as a diagnostic tool.

关键词： Learning-to-rank   machine learning   supervised learning   artificial intelligence  

摘要： Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.

关键词： machine learning   microbial risk assessment   support vector machines   whole genome sequencing  

摘要： Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89%[95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA. [ABSTRACT FROM AUTHOR]

关键词： Vehicle ownership   Phnom Penh   Features ranking   Multinomial logit model   Neural networks   Random forests  

摘要： Vehicle ownership patterns and their determinants play an important role in transportation policy-making. This issue has been paid even greater attention in developing countries that aspire to reach sustainable transportation development goals in the era of urbanization and globalization. In this study, the multinomial logit model, neural networks and random forest were applied to examine the features' impact level and to also predict vehicle ownership patterns in Phnom Penh city. The empirical results indicate that household income is the most powerful variable affecting motorization in Phnom Penh. Supplementation of individual trip characteristics such as total number of trips made, number of trips made for work purposes and overall travel distance all make effective contributions as classifiers. Furthermore, it is acknowledged that the machine-learning approach outperformed not only in terms of predicting accuracy, but also in dealing with unbalanced categories when compared with the statistical approach. This detection supplies the advantages of applying machine learning techniques in terms of, but not limited to, the field of vehicle ownership.

关键词： methods: data analysis   techniques: photometric   catalogues   galaxies: distances and redshifts   galaxies: photometry  

摘要： Star formation rates (SFRs) are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised machine learning models. We demonstrate that, with such approach, accurate multiband photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky Survey Data Release 7. The catalogue will be made available through the Vizier facility.

关键词： Nonparametric regression   Local approximate Gaussian Process   Structure-property linkages   High-contrast composites   Gaussian Process regression  

摘要： Reduced-order structure-property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened new avenues for addressing this challenge. In this paper, we critically compare the relative merits of the application of four distinct machine learning approaches for their efficacy in extracting microstructure-property linkages from the finite element simulation data aggregated on high-contrast elastic composites with different microstructures. The machine learning approaches selected for the study have included different combinations of local/global and parametric/nonparametric approaches. Furthermore, the nonparametric approaches selected for this study are based on Gaussian Process (GP) models that allow for a formal treatment of uncertainty quantification in the predicted values. The predictive performances of these different approaches have been compared against each other using rigorous cross-validation error metrics. Furthermore, their sensitivity to both the dataset size and dimensionality has been investigated.

关键词： Spinal stenosis   Machine learning   Discharge placement   Rehabilitation   Calibration  

摘要： PurposeAn excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis. MethodsWe used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance *** included 28,600 patients with a median age of 67 (interquartile range 58-74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic=0.751), calibration (slope=0.933;intercept=0.037), and overall performance (Brier score=0.131).ConclusionsA machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower *** abstractThese slides can be retrieved under Electronic Supplementary Material. [GRAPHICS] .

关键词： Biomagnetics   boosting   feature extraction   ischemic heart disease detection and localization   machine learning   magnetocardiography   support vector machines  

摘要： Objective: This study focused on developing a fast and accurate automatic ischemic heart disease detection/localization methodology. Methods: T wave was segmented from averaged Magnetocardiography (MCG) recordings and 164 features were subsequently extracted. These features were categorized into three groups: time domain features, frequency domain features, and information theory features. Next, we compared different machine learning classifiers including: k-nearest neighbor, decision tree, support vector machine (SVM), and XGBoost. To identify ischemia heart disease (IHD) case, we selected three classifiers with best performance and applied model ensemble to average results. All 164 features were used in this stage. To localize ischemia, we classified IHD group according to stenosis locations, including left anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA). For this task, we used XGBoost classifier and 18 time domain features. Results: For IHD detection, the SVM-XGBoost model achieved best results with accuracy = 94.03%, precision = 86.56%, recall = 97.78%, F-score = 92.79%, AUC = 0.98, and average precision = 0.98. For ischemia localization, XGBoost model achieved accuracy = 0.74, 0.68, and 0.65, for LAD, LCX, and RCA, respectively. Conclusion: we have developed an automatic IHD detection and localization system. We find that 1. T wave repolarization synchronicity is an important factor to distinguish IHD from normal subjects 2. Magnetic field pattern is associated with stenosis location. Significance: The proposed machine learning method provides the clinicians a fast and accurate diagnosis tool to interpret MCG data, boosting its acceptance into clinics. Furthermore, the magnetic pole characteristics revealed by the method shows to be related to ischemia location, presenting the opportunity to noninvasively locate ischemia.

关键词： Flood susceptibility   Machine Learning   Multi-Criteria Decision-Making   GIS   China  

摘要： Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.

关键词： Machine learning   Mineral prospectivity mapping   GIS   Support vector machine   Artificial neural networks   Random forest  

摘要： Predictive modelling of mineral prospectivity using GIS is a valid and progressively more accepted tool for delineating reproducible mineral exploration targets. In this study, machine learning methods, including support vector machine (SVM), artificial neural networks (ANN) and random forest (RF), were employed to conduct GIS-based mineral prospectivity mapping of the Tongling ore district, eastern China. The mineral systems approach was used to translate our understanding of the skarn Cu mineral system into mappable exploration criteria, resulting in 12 predictor maps that represent source, transport, physical trap and chemical deposition processes critical for ore formation. Predictive SVM, ANN and RF models were trained by way of predictor maps, and corroborated using a 10-fold cross-validation. The overall performance of the resulting predictive models was assessed in both training and test datasets using a confusion matrix, set of statistical measurements, receiver operating characteristic curve, and success-rate curve. The assessment results indicate that the three machine learning models presented in this study achieved satisfactory performance levels characterized by high predictive accuracy. In addition, all models exhibited well interpretability that provided consistent ranking information about the relative importance of the evidential features contributing to the final predictions. In comparison, the RF model outperformed the SVM and ANN models, having achieved greater consistency with respect to variations in the model parameters and better predictive accuracy. Importantly, the RF model exhibited the highest predictive efficiency capturing most of the known deposits within the smallest prospective tracts. The above results suggest that the RF model is the most appropriate model for Cu potential mapping in the Tongling ore district, and, therefore, was used to generate a prospectivity map containing very-high, high, moderate, and low potential areas in