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Kriegler, B. and Berk, R. (2007) Boosting the Quantile Distribution: A Cost-Sensitive Statistical Learning Procedure. Technical Report, Department of Statitics, University of California, Los Angeles.

has been cited by the following article:

• TITLE: An Efficient Smooth Quantile Boost Algorithm for Binary Classification

  AUTHORS: Zhefeng Wang, Wanzhou Ye

  KEYWORDS: Boosting, Quantile Regression, Smooth Check Function, Binary Classification

  JOURNAL NAME: Advances in Pure Mathematics, Vol.6 No.9, August 24, 2016

  ABSTRACT: In this paper, we propose a Smooth Quantile Boost Classification (SQBC) algorithm for binary classification problem. The SQBC algorithm directly uses a smooth function to approximate the “check function” of the quantile regression. Compared to other boost-based classification algorithms, the proposed algorithm is more accurate, flexible and robust to noisy predictors. Furthermore, the SQBC algorithm also can work well in high dimensional space. Extensive numerical experiments show that our proposed method has better performance on randomly simulations and real data.