IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v79y2017i4p959-1035.html

Random-projection ensemble classification

Author

Listed:
  • Timothy I. Cannings
  • Richard J. Samworth

Abstract

No abstract is available for this item.

Suggested Citation

  • Timothy I. Cannings & Richard J. Samworth, 2017. "Random-projection ensemble classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 959-1035, September.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:4:p:959-1035
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssb.12228
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    2. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2014. "Probability-enhanced sufficient dimension reduction for binary classification," Biometrics, The International Biometric Society, vol. 70(3), pages 546-555, September.
    3. Blaser, Rico & Fryzlewicz, Piotr, 2016. "Random rotation ensembles," LSE Research Online Documents on Economics 62182, London School of Economics and Political Science, LSE Library.
    4. Garritt Page & Abhishek Bhattacharya & David Dunson, 2013. "Classification via Bayesian Nonparametric Learning of Affine Subspaces," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 187-201, March.
    5. Frank Critchley & Willem Heiser, 1988. "Hierarchical trees can be perfectly scaled in one dimension," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 5-20, March.
    6. Y. Yu & T. Wang & R. J. Samworth, 2015. "A useful variant of the Davis–Kahan theorem for statisticians," Biometrika, Biometrika Trust, vol. 102(2), pages 315-323.
    7. Yannis Yatracos, 2013. "Detecting Clusters in the Data from Variance Decompositions of Its Projections," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 30-55, April.
    8. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    9. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    10. Adrien Jamain & David Hand, 2008. "Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 87-112, June.
    11. Roopesh Ranjan & Tilmann Gneiting, 2010. "Combining probability forecasts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 71-91, January.
    12. He, Yong & Zhang, Xinsheng & Wang, Pingping, 2016. "Discriminant analysis on high dimensional Gaussian copula model," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 100-112.
    13. Martha Bohorquez & Ramon Giraldo & Jorge Mateu, 2016. "Optimal dynamic spatial sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 27(5), pages 293-305, August.
    14. Hall, Peter & Xue, Jing-Hao, 2014. "On selecting interacting features from high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 694-708.
    15. C. Hennig & C. Viroli, 2016. "Quantile-based classifiers," Biometrika, Biometrika Trust, vol. 103(2), pages 435-446.
    16. Fang, Hong-Bin & Fang, Kai-Tai & Kotz, Samuel, 2002. "The Meta-elliptical Distributions with Given Marginals," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 1-16, July.
    17. Montanari, Angela & Lizzani, Laura, 2001. "A projection pursuit approach to variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 463-473, February.
    18. Thulin, Måns, 2014. "A high-dimensional two-sample test for the mean using random subspaces," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 26-38.
    19. Patricia M. E. Altham, 1978. "Two Generalizations of the Binomial Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(2), pages 162-167, June.
    20. Peter Hall & Richard J. Samworth, 2005. "Properties of bagged nearest neighbour classifiers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 363-379, June.
    21. Rajarshi Guhaniyogi & David B. Dunson, 2015. "Bayesian Compressed Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1500-1514, December.
    22. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    23. Yatracos, Yannis, 2002. "Assessing the quality of bootstrap samples and of the bootstrap estimates obtained with finite resampling," Statistics & Probability Letters, Elsevier, vol. 59(3), pages 281-292, October.
    24. Jianqing Fan & Yang Feng & Xin Tong, 2012. "A road to classification in high dimensional space: the regularized optimal affine discriminant," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 745-771, September.
    25. Biau, Gérard & Devroye, Luc, 2010. "On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2499-2518, November.
    26. Baibing Li, 2006. "A new approach to cluster analysis: the clustering‐function‐based method," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 457-476, June.
    27. Rajen D. Shah & Richard J. Samworth, 2013. "Variable selection with error control: another look at stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 55-80, January.
    28. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    29. Hall, Peter & Titterington, D. M. & Xue, Jing-Hao, 2009. "Median-Based Classifiers for High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1597-1608.
    30. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    31. Will Wei Sun & Xingye Qiao & Guang Cheng, 2016. "Stabilized Nearest Neighbor Classifier and its Statistical Properties," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1254-1265, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fuli Zhang & Kung‐Sik Chan, 2023. "Random projection ensemble classification with high‐dimensional time series," Biometrics, The International Biometric Society, vol. 79(2), pages 964-974, June.
    2. Bergsma, Wicher P, 2020. "Regression with I-priors," Econometrics and Statistics, Elsevier, vol. 14(C), pages 89-111.
    3. Jiang, Qing & Hušková, Marie & Meintanis, Simos G. & Zhu, Lixing, 2019. "Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 202-220.
    4. Zardad Khan & Asma Gul & Aris Perperoglou & Miftahuddin Miftahuddin & Osama Mahmoud & Werner Adler & Berthold Lausen, 2020. "Ensemble of optimal trees, random forest and random projection ensemble classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 97-116, March.
    5. Laura Anderlucci & Francesca Fortunato & Angela Montanari, 2022. "High-Dimensional Clustering via Random Projections," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 191-216, March.
    6. Jingxuan Luo & Xuejiao Li & Chongxiu Yu & Gaorong Li, 2023. "Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure Among Predictors," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 614-637, November.
    7. Nayiri Galestian Pour & Soudabeh Shemehsavar, 2024. "Learning from high dimensional data based on weighted feature importance in decision tree ensembles," Computational Statistics, Springer, vol. 39(1), pages 313-342, February.
    8. Wu, Ruiyang & Hao, Ning, 2022. "Quadratic discriminant analysis by projection," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    9. Hu, Jianhua & Li, Tao & Liu, Xiaoqian & Liu, Xu, 2025. "Random projection-based response best-subset selector for ultra-high dimensional multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 210(C).
    10. Deepak Nag Ayyala & Santu Ghosh & Daniel F. Linder, 2022. "Covariance matrix testing in high dimension using random projections," Computational Statistics, Springer, vol. 37(3), pages 1111-1141, July.
    11. Bergsma, Wicher, 2020. "Regression with I-priors," LSE Research Online Documents on Economics 102136, London School of Economics and Political Science, LSE Library.
    12. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    13. Hong-Bin Fang & Hengzhen Huang & Ao Yuan & Ruzong Fan & Ming T. Tan, 2024. "Integrative Classification Using Structural Equation Modeling of Homeostasis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(3), pages 742-760, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    2. Martin, Gael M. & Loaiza-Maya, Rubén & Maneesoonthorn, Worapree & Frazier, David T. & Ramírez-Hassan, Andrés, 2022. "Optimal probabilistic forecasts: When do they work?," International Journal of Forecasting, Elsevier, vol. 38(1), pages 384-406.
    3. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    4. Borgonovo, Emanuele & Jose, Victor Richmond R. & Knowlton, Morgan & Shachter, Ross & Siebert, Johannes Ulrich & Ulu, Canan, 2026. "Fifty years of decision analysis in operational research: A review," European Journal of Operational Research, Elsevier, vol. 329(2), pages 355-377.
    5. Pascal Kundig & Fabio Sigrist, 2024. "A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios," Papers 2410.02846, arXiv.org, revised Dec 2025.
    6. Sahab Zandi & Kamesh Korangi & Juan C. Moreno-Paredes & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2025. "A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks," Papers 2510.09407, arXiv.org.
    7. Václav Klepáč, 2015. "Default Probability Prediction with Static Merton-D-Vine Copula Model," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 1(2), pages 104-113.
    8. Alexander Henzi & Johanna F Ziegel, 2022. "Valid sequential inference on probability forecast performance [A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems]," Biometrika, Biometrika Trust, vol. 109(3), pages 647-663.
    9. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    10. Neuberg Richard & Hannah Lauren, 2017. "Loan pricing under estimation risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 69-87, June.
    11. Ruben Loaiza‐Maya & Gael M. Martin & David T. Frazier, 2021. "Focused Bayesian prediction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 517-543, August.
    12. Shaikh, Ibrahim A. & O'Brien, Jonathan Paul & Peters, Lois, 2018. "Inside directors and the underinvestment of financial slack towards R&D-intensity in high-technology firms," Journal of Business Research, Elsevier, vol. 82(C), pages 192-201.
    13. José-Luis Peydró [AP BACKUP – NOW EXTERNAL] & Mikel Bedayo & Raquel Vegas & Gabriel Jiménez & José-Luis Peydró, 2020. "Screening and Loan Origination Time: Lending Standards, Loan Defaults and Bank Failures," Working Papers 1215, Barcelona School of Economics.
    14. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    15. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    16. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    17. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    18. Dinh, K. & Kleimeier, S., 2006. "Credit scoring for Vietnam's retail banking market : implementation and implications for transactional versus relationship lending," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    19. Gao, Xin & Xu, Weidong & Li, Donghui, 2025. "Media coverage and managerial investment learning from stock markets: International evidence," Research in International Business and Finance, Elsevier, vol. 76(C).
    20. Elizaveta Danilova & Evgeny Rumyantsev & Ivan Shevchuk, 2018. "Review of the Bank of Russia – IMF Workshop 'Recent Developments in Macroprudential Stress Testing'," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 60-83, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssb:v:79:y:2017:i:4:p:959-1035. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.