Multi-Stage Prediction for Zero-Inflated Hurricane Induced Power Outages
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Seth D. Guikema | Chengwei Zhai | Sara Shashaani | Steven M. Quiring | Jordan V. Pino | S. Guikema | S. Quiring | S. Shashaani | Chengwei Zhai | J. V. Pino
[1] D. Wanik,et al. Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network , 2017, Risk analysis : an official publication of the Society for Risk Analysis.
[2] Zhi-Hua Zhou,et al. Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .
[3] Seth D. Guikema,et al. Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data , 2012, Reliab. Eng. Syst. Saf..
[4] Yue-Shi Lee,et al. Under-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset , 2006 .
[5] Jery R. Stedinger,et al. Negative Binomial Regression of Electric Power Outages in Hurricanes , 2005 .
[6] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[7] Q. Mcnemar. Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.
[8] Rachel A. Davidson,et al. Electric Power Distribution System Performance in Carolina Hurricanes , 2003 .
[9] R. Barandelaa,et al. Strategies for learning in class imbalance problems , 2003, Pattern Recognit..
[10] D. Wanik,et al. Storm outage modeling for an electric distribution network in Northeastern USA , 2015, Natural Hazards.
[11] Alan Agresti,et al. Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.
[12] Seth D. Guikema,et al. Predicting Hurricane Power Outages to Support Storm Response Planning , 2014, IEEE Access.
[13] Haibin Liu,et al. Statistical Forecasting of Electric Power Restoration Times in Hurricanes and Ice Storms , 2007, IEEE Transactions on Power Systems.
[14] A. Pahwa,et al. Modeling Weather-Related Failures of Overhead Distribution Lines , 2007, 2007 IEEE Power Engineering Society General Meeting.
[15] David A. Cieslak,et al. Learning Decision Trees for Unbalanced Data , 2008, ECML/PKDD.
[16] Nicola Torelli,et al. Training and assessing classification rules with imbalanced data , 2012, Data Mining and Knowledge Discovery.
[17] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[18] Seth D Guikema,et al. Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors , 2018, Risk analysis : an official publication of the Society for Risk Analysis.
[19] S. Guikema,et al. Statistical models of the effects of tree trimming on power system outages , 2006, IEEE Transactions on Power Delivery.
[20] Andreas Stolcke,et al. A study in machine learning from imbalanced data for sentence boundary detection in speech , 2006, Comput. Speech Lang..
[21] Zhi-Hua Zhou,et al. Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).
[22] Roshanak Nateghi,et al. Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models , 2014, Risk analysis : an official publication of the Society for Risk Analysis.
[23] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[24] Seth D Guikema,et al. Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems , 2010, Risk analysis : an official publication of the Society for Risk Analysis.
[25] Vasile Palade,et al. FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning , 2010, IEEE Transactions on Fuzzy Systems.
[26] Zhi-Hua Zhou,et al. Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[27] Seth D. Guikema,et al. Importance of soil and elevation characteristics for modeling hurricane-induced power outages , 2011 .
[28] Seth D Guikema,et al. Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk , 2016, Risk analysis : an official publication of the Society for Risk Analysis.
[29] Kai Ming Ting,et al. An Instance-weighting Method to Induce Cost-sensitive Trees , 2001 .
[30] D. Wanik,et al. Using vegetation management and LiDAR-derived tree height data to improve outage predictions for electric utilities , 2017 .
[31] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[32] Haibin Liu,et al. Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms , 2008, Reliab. Eng. Syst. Saf..
[33] Seth D Guikema,et al. Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models , 2009, Risk analysis : an official publication of the Society for Risk Analysis.
[34] Francisco Herrera,et al. Evolutionary-based selection of generalized instances for imbalanced classification , 2012, Knowl. Based Syst..
[35] Seth D. Guikema,et al. Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region , 2009, Reliab. Eng. Syst. Saf..
[36] David Mease,et al. Boosted Classification Trees and Class Probability/Quantile Estimation , 2007, J. Mach. Learn. Res..