Winning the KDD99 classification cup: bagged boosting
Abstract
We briefly describe our approach for the KDD99 Classification Cup. The solution is essentially a mixture of bagging and boosting. Additionally, asymmetric error costs are taken into account by minimizing the so-called conditional risk . Furthermore, the standard sampling with replacement methodology of bagging was modified to put a specific focus on the smaller but expensive-if-predicted-wrongly classes.
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The KDD cup winner's proposed approach performs poorly for U2R and R2L attack categories.
“Clearly, the proposed approach performs poorly for U2R and R2L attack categories.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [358]
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Pattern classification and scene analysis
Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
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- Authors
- Pfahringer, B.
- Affiliations
- Austrian Research Institute for Al Schottengasse 3, Vienna, Austria
- Corresponding author
- Pfahringer, Bernhard
- ORCID
- Pfahringer, Bernhard
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2000-01
- Volume
- 1
- Issue
- 2
- Pages
- 65-66
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W1995126785
- Cited by
- 216
- References
- 1
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 15 · 2020: 7 · 2019: 12 · 2018: 6 · 2017: 7 · 2016: 14 · 2015: 12 · 2014: 12 · 2013: 7 · 2012: 15
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- bagging, boosting, sampling with replacement, class imbalance, ensemble learning
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