Using an improved C4.5 for imbalanced dataset of intrusion

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Using an improved C4.5 for imbalanced dataset of intrusion

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PST; Vol. 380 archive
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services table of contents

Markham, Ontario, Canada

SESSION: Short papers: Security applications table of contents

Article No. 67

Year of Publication: 2006

ISBN:1-59593-604-1

Authors

Quan Zhou	Nanjing University, Nanjing
Lin-gang Gu	Jiangsu Provincial Security Bureau of PRC
Chong-jun Wang	Nanjing University, Nanjing
Wang-jun	Nanjing University, Nanjing
Shi-fu Chen	Nanjing University, Nanjing

Sponsor

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 5, Downloads (12 Months): 30, Citation Count: 0

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abstract references collaborative colleagues

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ABSTRACT

The imbalance of dataset will directly affect the precision of classifier. PC4.5, an improved C4.5 algorithm is proposed. The experiments in MIT dataset indicate that PC4.5 is effective on imbalanced dataset and the scale of the decision tree could be reduced.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
	2	Zhang Qi-rui, Zhang Lin, Dong Shou-bin. The influence of class distribution on Text Categorization. Journal of Tsinghua University (Science and Technology). 2005 (45): 1802--1805
	3	Ian H. Witten , Eibe Frank, Data mining: practical machine learning tools and techniques with Java implementations, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000
	4	Chris Drummond & Robert C. Holte. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. in proceeding of Learning from Imbalanced Datasets II, ICML, Washington DC, 2003.
	5	Prati, R. C., Batista, G. E. A. P. A., and Monard, M. C. Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior. In MICAI (2004), pp. 312--321. LNAI 2972.
	6	A. Kolcz, A. Chowdhury and J. Alspector, "Data Duplication: An Imbalance Problem?," ICML'2003 Workshop on Learning from Imbalanced Datasets, Washington, DC, USA, 2003

Collaborative Colleagues:

Quan Zhou: colleagues

Lin-gang Gu: colleagues

Chong-jun Wang: colleagues

Wang-jun: colleagues

Shi-fu Chen: colleagues

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