FlexBoost: A Flexible Boosting Algorithm With Adaptive Loss Functions

Robust Loss Functions for Boosting

Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, ...

Boosting in the presence of outliers: adaptive classification with non-convex loss functions

This paper examines the role and efficiency of the non-convex loss functions for binary classification problems. In particular, we investigate how to design a simple and effective boosting algorithm that is robust to the outliers in the data. The analysis of the role of a particular non-convex loss for prediction accuracy varies depending on the diminishing tail properties of the gradient of th...

Adaptive Boosting for Spatial Functions with Unstable Driving Attributes

Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipulating training data distributions. Standard combining methods do not improve local classifiers (e.g. k-nearest neighbors) due to their low sensitivity to data perturbation. Here, we propose an adaptive attribute boos...

Boosting with log-loss

Oil Price Forecast Evaluation with Flexible Loss Functions

The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Sec...