Assessing risk in life insurance using ensemble learning

[1]  Wen Gao,et al.  Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition , 2012, IEEE Transactions on Image Processing.

[2]  T. Stijnen,et al.  Review: a gentle introduction to imputation of missing values. , 2006, Journal of clinical epidemiology.

[3]  James M. Carson,et al.  Sunk Costs and Screening: Two-Part Tariffs in Life Insurance , 2017 .

[4]  H. Abdi,et al.  Principal component analysis , 2010 .

[5]  Rui Xia,et al.  Ensemble of feature sets and classification algorithms for sentiment classification , 2011, Inf. Sci..

[6]  Mohd Rahimie Bin Md Noor,et al.  Predicting number of purchasing life insurance using Markov chain method , 2014 .

[7]  Estevam R. Hruschka,et al.  Tweet sentiment analysis with classifier ensembles , 2014, Decis. Support Syst..

[8]  G. Caporale,et al.  Analysing the Determinants of Insolvency Risk For General Insurance Firms in the UK , 2017 .

[9]  Pourya Shamsolmoali,et al.  Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier , 2015 .

[10]  Raafat S. Elfouly,et al.  NOVEL ENSEMBLE TECHNIQUES FOR REGRESSION WITH MISSING DATA , 2009 .

[11]  Guangquan Li,et al.  Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble , 2017, The Lancet.

[12]  M. Kenward,et al.  Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls , 2009, BMJ : British Medical Journal.

[13]  Asif Ekbal,et al.  Stacked ensemble coupled with feature selection for biomedical entity extraction , 2013, Knowl. Based Syst..

[14]  Jianqing Fan,et al.  A Selective Overview of Variable Selection in High Dimensional Feature Space. , 2009, Statistica Sinica.

[15]  T. Coleman,et al.  Auto insurance fraud detection using unsupervised spectral ranking for anomaly , 2016 .

[16]  Behrouz Minaei-Bidgoli,et al.  Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study , 2015, International journal of health policy and management.

[17]  Antonio Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, CVPR 2004.

[18]  Keke Gai,et al.  Security-Aware Information Classifications Using Supervised Learning for Cloud-Based Cyber Risk Management in Financial Big Data , 2016, 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS).

[19]  Guandong Xu,et al.  Automated Underwriting in Life Insurance: Predictions and Optimisation , 2018, ADC.

[20]  G. Gigerenzer,et al.  Risk, Uncertainty, and Heuristics , 2014 .

[21]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[22]  James Jin Kang,et al.  Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices , 2018, Technologies.

[23]  Hong Yan,et al.  Missing value imputation for gene expression data: computational techniques to recover missing data from available information , 2011, Briefings Bioinform..

[24]  Ajith Abraham,et al.  Overlap Function Based Fuzzified Aquatic Behaviour Information Extracted Tsunami Prediction Model , 2019, Int. J. Distributed Syst. Technol..

[25]  Usman Qamar,et al.  Global Optimization Ensemble Model for Classification Methods , 2014, TheScientificWorldJournal.

[26]  Zhenhua Guo,et al.  Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[28]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[29]  Seth Earley.  Big Data and Predictive Analytics: What's New? , 2014, IT Professional.

[30]  Chaoqing Yu,et al.  A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging , 2017 .

[31]  Adrian D. C. Chan,et al.  Myoelectric Control Development Toolbox , 2007 .

[32]  Manoj Jayabalan,et al.  Risk prediction in life insurance industry using supervised learning algorithms , 2018, Complex & Intelligent Systems.

[33]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[35]  L. Ceriani,et al.  The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini , 2012 .

[36]  Eibe Frank,et al.  Introducing Machine Learning Concepts with WEKA , 2016, Statistical Genomics.

[37]  Kathleen McGarry,et al.  Multiple Dimensions of Private Information: Evidence from the Long-Term Care Insurance Market. , 2006, The American economic review.

[38]  Deepali Virmani,et al.  Ensemble learning using fast rule based fuzzy K –means pre clustering and classification for aquatic behavior-extracted tsunami prediction , 2019 .

[39]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[40]  H. Lookman Sithic,et al.  Survey of Insurance Fraud Detection Using Data Mining Techniques , 2013, ArXiv.

[41]  Kin Keung Lai,et al.  Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..

[42]  Keiji Yasuda,et al.  Prediction Models for Risk of Type-2 Diabetes Using Health Claims , 2018, BioNLP.

[43]  T. Iizumi,et al.  Global crop yield forecasting using seasonal climate information from a multi-model ensemble , 2018, Climate Services.