Projects per year
Abstract
We provide a unifying perspective for two decades of work on cost-sensitive Boosting algorithms. When analyzing the literature 1997–2016, we find 15 distinct cost-sensitive variants of the original algorithm; each of these has its own motivation and claims to superiority—so who should we believe? In this work we critique the Boosting literature using four theoretical frameworks: Bayesian decision theory, the functional gradient descent view, margin theory, and probabilistic modelling. Our finding is that only three algorithms are fully supported—and the probabilistic model view suggests that all require their outputs to be calibrated for best performance. Experiments on 18 datasets across 21 degrees of imbalance support the hypothesis—showing that once calibrated, they perform equivalently, and outperform all others. Our final recommendation—based on simplicity, flexibility and performance—is to use the original Adaboost algorithm with a shifted decision threshold and calibrated probability estimates.
Original language | English |
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Pages (from-to) | 359-384 |
Number of pages | 26 |
Journal | Machine Learning |
Volume | 104 |
Issue number | 2 |
Early online date | 2 Aug 2016 |
DOIs | |
Publication status | Published - 1 Sept 2016 |
Research Groups and Themes
- Jean Golding
- SPHERE
Keywords
- Boosting
- Class imbalance
- Classifier calibration
- Cost-sensitive
Fingerprint
Dive into the research topics of 'Cost-sensitive boosting algorithms: Do we really need them?'. Together they form a unique fingerprint.Projects
- 2 Finished
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SPHERE (EPSRC IRC)
Craddock, I. J. (Principal Investigator), Coyle, D. T. (Principal Investigator), Flach, P. A. (Principal Investigator), Kaleshi, D. (Principal Investigator), Mirmehdi, M. (Principal Investigator), Piechocki, R. J. (Principal Investigator), Stark, B. H. (Principal Investigator), Ascione, R. (Co-Principal Investigator), Ashburn, A. M. (Collaborator), Burnett, M. E. (Collaborator), Damen, D. (Co-Principal Investigator), Gooberman-Hill, R. (Principal Investigator), Harwin, W. S. (Collaborator), Hilton, G. (Co-Principal Investigator), Holderbaum, W. (Collaborator), Holley, A. P. (Manager), Manchester, V. A. (Administrator), Meller, B. J. (Other ), Stack, E. (Collaborator) & Gilchrist, I. D. (Principal Investigator)
1/10/13 → 30/09/18
Project: Research, Parent
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REFrAMe
Flach, P. A. (Principal Investigator)
Engineering and Physical Sciences Research Council
1/02/13 → 1/08/16
Project: Research
Profiles
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Professor Peter A Flach
- School of Computer Science - Professor of Artificial Intelligence
- Intelligent Systems Laboratory
Person: Academic , Member