IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v98y2016icp46-59.html
   My bibliography  Save this article

High dimensional classifiers in the imbalanced case

Author

Listed:
  • Bak, Britta Anker
  • Jensen, Jens Ledet

Abstract

A binary classification problem is imbalanced when the number of samples from the two groups differs. For the high dimensional case, where the number of variables is much larger than the number of samples, imbalance leads to a bias in the classification. The independence classifier is studied theoretically and based on the analysis two new classifiers are suggested that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some aspects outperform multiple undersampling. For correlated data the ROAD classifier is considered and a suggestion is given for how to modify the classifier to handle the bias from imbalanced group sizes.

Suggested Citation

  • Bak, Britta Anker & Jensen, Jens Ledet, 2016. "High dimensional classifiers in the imbalanced case," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 46-59.
    • Handle: RePEc:eee:csdana:v:98:y:2016:i:c:p:46-59
    DOI: 10.1016/j.csda.2015.12.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315003175
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2015.12.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Britta Anker Bak & Jens Ledet Jensen & Morten Fenger-Grøn, 2015. "Classification Error of the Thresholded Independence Rule," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 32-42, March.
    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. Abpeykar, Shadi & Ghatee, Mehdi & Zare, Hadi, 2019. "Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 12-36.

    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. Oda, Ryoya & Suzuki, Yuya & Yanagihara, Hirokazu & Fujikoshi, Yasunori, 2020. "A consistent variable selection method in high-dimensional canonical discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    2. Jianqing Fan & Yang Feng & Jiancheng Jiang & Xin Tong, 2016. "Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 275-287, March.
    3. Zeyu Wu & Cheng Wang & Weidong Liu, 2023. "A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 619-648, August.
    4. Qiang Sun & Hongtu Zhu & Yufeng Liu & Joseph G. Ibrahim, 2015. "SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 289-302, March.
    5. Yixin Fang & Yang Feng & Ming Yuan, 2014. "Regularized principal components of heritability," Computational Statistics, Springer, vol. 29(3), pages 455-465, June.
    6. Liu, Jianyu & Yu, Guan & Liu, Yufeng, 2019. "Graph-based sparse linear discriminant analysis for high-dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 250-269.
    7. Shen, Yanfeng & Lin, Zhengyan, 2015. "An adaptive test for the mean vector in large-p-small-n problems," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 25-38.
    8. Yaqiong Cui & Jukka Sirén & Timo Koski & Jukka Corander, 2016. "Simultaneous Predictive Gaussian Classifiers," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 73-102, April.
    9. Hongtu Zhu & Dan Shen & Xuewei Peng & Leo Yufeng Liu, 2017. "MWPCR: Multiscale Weighted Principal Component Regression for High-Dimensional Prediction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1009-1021, July.
    10. Nickolay T. Trendafilov & Tsegay Gebrehiwot Gebru, 2016. "Recipes for sparse LDA of horizontal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 207-221, August.
    11. Irina Gaynanova & James G. Booth & Martin T. Wells, 2016. "Simultaneous Sparse Estimation of Canonical Vectors in the ≫ Setting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 696-706, April.
    12. Luigi Ippoliti & Simone Di Zio & Arcangelo Merla, 2014. "Classification of biomedical signals for differential diagnosis of Raynaud's phenomenon," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1830-1847, August.
    13. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    14. 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.
    15. Pan, Yuqing & Mai, Qing, 2020. "Efficient computation for differential network analysis with applications to quadratic discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    16. Sheng, Ying & Wang, Qihua, 2019. "Simultaneous variable selection and class fusion with penalized distance criterion based classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 138-152.
    17. Shuaishuai Chen & Jun Lu, 2023. "Quantile-Composited Feature Screening for Ultrahigh-Dimensional Data," Mathematics, MDPI, vol. 11(10), pages 1-21, May.
    18. Sihai Dave Zhao, 2017. "Integrative genetic risk prediction using non-parametric empirical Bayes classification," Biometrics, The International Biometric Society, vol. 73(2), pages 582-592, June.
    19. Jung, Sungkyu, 2018. "Continuum directions for supervised dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 27-43.
    20. Makoto Aoshima & Kazuyoshi Yata, 2019. "High-Dimensional Quadratic Classifiers in Non-sparse Settings," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 663-682, September.

    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:eee:csdana:v:98:y:2016:i:c:p:46-59. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.