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Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class
[article]
2016
arXiv
pre-print
We present a simple unified framework for multi-class cost-sensitive boosting. The minimum-risk class is estimated directly, rather than via an approximation of the posterior distribution. Our method jointly optimizes binary weak learners and their corresponding output vectors, requiring classes to share features at each iteration. By training in a cost-sensitive manner, weak learners are invested in separating classes whose discrimination is important, at the expense of less relevant
arXiv:1607.03547v2
fatcat:y5ebtcywyrgtdly3y2mxbydkny