QBoost: Predicting quantiles with boosting for regression and binary classification

In the framework of functional gradient descent/ascent, this paper proposes Quantile Boost (QBoost) algorithms which predict quantiles of the interested response for regression and binary classification. Quantile Boost Regression performs gradient descent in functional space to minimize the objective function used by quantile regression (QReg). In the classification scenario, the class label is defined via a hidden variable, and the quantiles of the class label are estimated by fitting the corresponding quantiles of the hidden variable. An equivalent form of the definition of quantile is introduced, whose smoothed version is employed as the objective function, and then maximized by functional gradient ascent to obtain the Quantile Boost Classification algorithm. Extensive experimentation and detailed analysis show that QBoost performs better than the original QReg and other alternatives for regression and binary classification. Furthermore, QBoost is capable of solving problems in high dimensional space and is more robust to noisy predictors.

Figure optionsDownload as PowerPoint slideHighlights
► Applies functional gradient boosting to quantile regression model.
► Generalizes the proposed method for binary classification problems.
► Can be applied to high dimensional data, and to identify informative variables.
► Extensively tested on benchmark machine learning data, gene data, and face data.
► Performs better than the original quantile regression, robust to noisy predictors.