Efficient Multiclass Boosting Classification with Active Learning

Date

2007-01-01

Author

Huang, Jian
Ertekin Bolelli, Şeyda
Song, Yang
Zha, Hongyuan
Giles, C. Lee

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We propose a novel multiclass classification algorithm Gentle Adaptive Multiclass Boosting Learning (GAMBLE). The algorithm naturally extends the two class Gentle AdaBoost algorithm to multiclass classification by using the multiclass exponential loss and the multiclass response encoding scheme. Unlike other multiclass algorithms which reduce the K-class classification task to K binary classifications, GAMBLE handles the task directly and symmetrically, with only one committee classifier. We formally derive the GAM-BLE algorithm with the quasi-Newton method, and prove the structural equivalence of the two regression trees in each boosting step.

Subject Keywords

Committee Machines, Data Mining, Active Learning, Multiclass Classification, Boosting

URI

https://hdl.handle.net/11511/54186

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

J. Huang, Ş. Ertekin Bolelli, Y. Song, H. Zha, and C. L. Giles, “Efficient Multiclass Boosting Classification with Active Learning,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54186.