.png){kind=icon}
Return
Multi-class boosting with asymmetric binary weak-learners
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2013.11.024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We introduce a multi-class generalization of AdaBoost with binary weak-learners. We use a vectorial codification to represent class labels and a multi-class exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of punishments for failures and rewards for successes. Moreover, the stage-wise optimization of this model introduces an asymmetric boosting procedure whose costs depend on the number of classes separated by each weak-learner. In this way the boosting algorithm takes into account class imbalances when building the ensemble. The experiments performed compare this new approach favorably to AdaBoost.MH, Gentle-Boost and the SAMME algorithms. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
AdaBoost
Multi-class classification
Asymmetric binary weak-learners
Class imbalance
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
Bootstrapping Boosted Random Ferns for discriminative and efficient object classification.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAIShareSaveNeuronal-binding antibodies from patients with antiphospholipid syndrome induce cognitive deficits following intrathecal passive transferLupus
LupusIF0
PREAI
ShareSaveBoosted key-frame selection and correlated pyramidal motion-feature representation for human action recognitionPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSave
View more