Cost Sensitive Boosting - University of California, San Diego.

The proposed framework is applied to the derivation of cost-sensitive extensions of AdaBoost, RealBoost, and LogitBoost. Experimental evidence, with a synthetic problem, standard data sets, and the computer vision problems of face and car detection, is presented in support of the cost-sensitive optimality of the new algorithms.

Cost-sensitive-Boosting-Tutorial.. (AdaBoost with calibrated probability estimates and a shifted decision threshold) found to be the most flexible, empirically successful and theoretically valid way to handle asymmetric classification with adaboost ensembles. The code provided allows the user to reproduce the papers experiments, but also to.

A Brief Introduction to Adaboost Hongbo Deng 6 Feb, 2007. Final classification based on weighted vote of weak classifiers. 9 Adaboost Terminology. Sensitive to noisy data and outliers. 26 References.

In the last few years, a lot of approaches have been proposed to provide standard AdaBoost with cost-sensitive capabilities, each with a different focus. However, for the researcher, these algorithms shape a tangled set with diffuse differences and properties, lacking a unifying analysis to jointly compare, classify, evaluate and discuss those approaches on a common basis.

All the cost-sensitive boosters are studied and five new extensions are proposed and their results are compared in this paper. A few future extensions are notified.

To solve the cost merging problem when multi-class cost-sensitive classification is transferred to two-class cost-sensitive classification, a cost-sensitive AdaBoost algorithm which can be applied directly to multi-class classification is constructed. The.

Cost-sensitive learning is a subfield of machine learning that takes the costs of prediction errors (and potentially other costs) into account when training a machine learning model. It is a field of study that is closely related to the field of imbalanced learning that is concerned with classification on datasets with a skewed class distribution.

In this paper, the AdaBoost algorithm is adapted for advancing the classification of imbalanced data. Three cost-sensitive boosting algorithms are developed by introducing cost items into the learning framework of AdaBoost.

Minimization of these losses leads to cost sensitive extensions of the popular AdaBoost, RealBoost, and LogitBoost algorithms. Experimental validation, on various UCI datasets and the computer vision problem of face detection, shows that the new algorithms substantially improve performance over what was achievable with previous cost-sensitive boosting approaches.

In order to clarify the role of AdaBoost algorithm for feature selection, classifier learning and its relation with SVM, this paper provided a brief introduction to the AdaBoost which is used for producing a strong classifier out of weak learners firstly.

Cost-sensitive boosters are studied and three new extensions of CostBoost algorithm CBE1, CBE2 and CBE3 are proposed and compared with existing cost based boosting classifiers. CSE1, CSE2 and CSE3 outperformed the original CostBoost by 5%, 4% and 4% respectively, in terms of misclassification cost.