A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset.
暂无分享,去创建一个
Matthias Schlögl | Michael Melcher | Gregor Laaha | Rainer Stütz | M. Melcher | M. Schlögl | G. Laaha | Rainer Stütz
[1] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[2] Carla E. Brodley,et al. Class Imbalance, Redux , 2011, 2011 IEEE 11th International Conference on Data Mining.
[3] Bartosz Krawczyk,et al. Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets , 2016, Pattern Recognit..
[4] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[5] Max Kuhn,et al. Applied Predictive Modeling , 2013 .
[6] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[7] George Yannis,et al. A review of the effect of traffic and weather characteristics on road safety. , 2014, Accident; analysis and prevention.
[8] P. Rietveld,et al. The impact of climate change and weather on transport: An overview of empirical findings , 2009 .
[9] F. Feroz,et al. MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics , 2008, 0809.3437.
[10] Jose Weissmann,et al. Exploring rainfall impacts on the crash risk on Texas roadways: A crash-based matched-pairs analysis approach. , 2018, Accident; analysis and prevention.
[11] Dave Winkler,et al. Bayesian Regularization of Neural Networks , 2009, Artificial Neural Networks.
[12] William N. Venables,et al. Modern Applied Statistics with S , 2010 .
[13] Julia B Edwards,et al. WEATHER-RELATED ROAD ACCIDENTS IN ENGLAND AND WALES: A SPATIAL ANALYSIS / , 1996 .
[14] A. Merloni,et al. X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue , 2014, 1402.0004.
[15] Matthias Schlögl,et al. Methodological considerations with data uncertainty in road safety analysis. , 2017, Accident; analysis and prevention.
[16] Georg Heinze,et al. A comparative investigation of methods for logistic regression with separated or nearly separated data , 2006, Statistics in medicine.
[17] Benjamin Hofner,et al. Model-based boosting in R: a hands-on tutorial using the R package mboost , 2012, Computational Statistics.
[18] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[19] Daniel J. Graham,et al. Road traffic accident prediction modelling: a literature review , 2017 .
[20] Bernhard Steinauer,et al. The Weighted Longitudinal Profile , 2008 .
[21] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[22] Daniel J. Nordman,et al. Case-Specific Random Forests , 2016 .
[23] Feng Chen,et al. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. , 2018, Journal of safety research.
[24] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[25] Torsten Hothorn,et al. Boosting additive models using component-wise P-Splines , 2008, Comput. Stat. Data Anal..
[26] George Yannis,et al. Explaining the road accident risk: weather effects. , 2013, Accident; analysis and prevention.
[27] J. Andrey,et al. Weather as a Chronic Hazard for Road Transportation in Canadian Cities , 2003 .
[28] George Yannis,et al. Predicting road accidents: a rare-events modeling approach , 2016 .
[29] D. Firth. Bias reduction of maximum likelihood estimates , 1993 .
[30] Geert Wets,et al. Studying the effect of weather conditions on daily crash counts using a discrete time-series model. , 2008, Accident; analysis and prevention.
[31] Peter Willett,et al. Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of Compounds , 1999, J. Comput. Biol..
[32] M. P. Hobson,et al. polychord: nested sampling for cosmology , 2015, Monthly Notices of the Royal Astronomical Society: Letters.
[33] Andreas Ziegler,et al. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.
[34] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[35] Evangelos Spiliotis,et al. Statistical and Machine Learning forecasting methods: Concerns and ways forward , 2018, PloS one.
[36] D. Eisenberg. The mixed effects of precipitation on traffic crashes. , 2004, Accident; analysis and prevention.
[37] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[38] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[39] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[40] Peter Buhlmann,et al. BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING , 2007, 0804.2752.
[41] D. Sculley,et al. Large Scale Learning to Rank , 2009 .
[42] F. Mannering. Temporal instability and the analysis of highway accident data , 2018 .
[43] Fred L. Mannering,et al. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .
[44] Chandra R. Bhat,et al. Analytic methods in accident research: Methodological frontier and future directions , 2014 .
[45] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[46] K. Hornik,et al. Unbiased Recursive Partitioning: A Conditional Inference Framework , 2006 .
[47] F Mannering,et al. Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. , 1995, Accident; analysis and prevention.
[48] Dirk Van den Poel,et al. bayesQR: A Bayesian Approach to Quantile Regression , 2017 .
[49] Achim Zeileis,et al. Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.
[50] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[51] Dirk Van den Poel,et al. Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution , 2012 .
[52] A. Lasenby,et al. polychord: next-generation nested sampling , 2015, 1506.00171.
[53] M J Maher,et al. A comprehensive methodology for the fitting of predictive accident models. , 1996, Accident; analysis and prevention.
[54] J. Friedman. Stochastic gradient boosting , 2002 .
[55] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[56] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[57] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[58] Athanasios Theofilatos,et al. Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. , 2017, Journal of safety research.
[59] Michael J Pencina,et al. Discrimination slope and integrated discrimination improvement – properties, relationships and impact of calibration , 2017, Statistics in medicine.
[60] J. Friedman. Multivariate adaptive regression splines , 1990 .
[61] Martin T. Hagan,et al. Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).
[62] P. Jones,et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .
[63] M. Schemper,et al. A solution to the problem of separation in logistic regression , 2002, Statistics in medicine.
[64] D. Cox. The Regression Analysis of Binary Sequences , 1958 .
[65] Rahim Alhamzawi,et al. Bayesian lasso binary quantile regression , 2013, Comput. Stat..
[66] Chandra R. Bhat,et al. Unobserved heterogeneity and the statistical analysis of highway accident data , 2016 .
[67] Francisco Bravo,et al. Real-time crash prediction in an urban expressway using disaggregated data , 2018 .
[68] R. Steinacker,et al. A Mesoscale Data Analysis and Downscaling Method over Complex Terrain , 2006 .
[69] Mohamed Abdel-Aty,et al. Crash risk analysis during fog conditions using real-time traffic data. , 2017, Accident; analysis and prevention.
[70] Vikash V. Gayah,et al. Crash Risk Assessment Using Intelligent Transportation Systems Data and Real-Time Intervention Strategies to Improve Safety on Freeways , 2007, J. Intell. Transp. Syst..
[71] Peter L. Bartlett,et al. Boosting Algorithms as Gradient Descent , 1999, NIPS.
[72] Jean Andrey,et al. Long-term trends in weather-related crash risks , 2010 .
[73] C. Manski. MAXIMUM SCORE ESTIMATION OF THE STOCHASTIC UTILITY MODEL OF CHOICE , 1975 .
[74] Mohamed Abdel-Aty,et al. Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network , 2019, Transportation Research Record: Journal of the Transportation Research Board.
[75] Daryl Lloyd,et al. Modelling weather effects on road casualty statistics , 2016 .
[76] Yves Deville,et al. DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization , 2012 .
[77] J. D. Malley,et al. Probability Machines , 2011, Methods of Information in Medicine.
[78] P Pérez-Rodríguez,et al. Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding. , 2013, Journal of animal science.
[79] Mohamed Abdel-Aty,et al. Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors. , 2013, Accident; analysis and prevention.
[80] Bernard Widrow,et al. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[81] Marco Vannucci,et al. A method for resampling imbalanced datasets in binary classification tasks for real-world problems , 2014, Neurocomputing.