[1]富春岩,葛茂松.一种能够适应概念漂移变化的数据流分类方法[J].智能系统学报,2007,2(4):86-91.
FU Chun-yan,GE Mao-song.A data stream classification methods adaptive to concept drift[J].CAAI Transactions on Intelligent Systems,2007,2(4):86-91.
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FU Chun-yan,GE Mao-song.A data stream classification methods adaptive to concept drift[J].CAAI Transactions on Intelligent Systems,2007,2(4):86-91.
一种能够适应概念漂移变化的数据流分类方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
2
期数:
2007年第4期
页码:
86-91
栏目:
学术论文—机器学习
出版日期:
2007-08-25
- Title:
- A data stream classification methods adaptive to concept drift
- 文章编号:
- 1673-4785(2007)04-0086-06
- Keywords:
- data streams; classification; concept drifting; onli n e learning; decision tree
- 分类号:
- TP311.13
- 文献标志码:
- A
- 摘要:
- 目前多数的数据流分类方法都是基于数据稳定分布这一假设，忽略了真实数据在一段时间内会发生潜在概念性的变化，这可能会降低分类模型的预测精度. 针对数据流的特性，提出一种能够识别并适应概念漂移发生的在线分类算法，实验表明它能根据目前概念漂移的状况，自动地调整训练窗口和模型重建期间新样本的个数.
- Abstract:
- At present, most classification methods for data streams are developed with the assumption of steady data distribution. However, the data collected fr om the real world will change over a period of time in the underlying concepts ( known as concept drifting). This lowers the predictive precision of a classifica tion model. This paper proposes a classification algorithm that can identify and adapt to occurrences of concept drifting according to the characteristics of the data stream. Experiments show that the proposed algorithm dynamically adjusts the size of the training window and the number of new examples during model rec onstruction according to the current rate of concept drifting.
- 参考文献/References:
-
［1］QUINLAN J R. Induction on decision trees［J］. Machine Learning,1986,13(1 ):81-106.
［2］QUINLAN J R.C4.5:programs for machine learning［M］.San Mateo:Morgan Kaufma nn, 1993.
［3］BREIMAN L,FRIEDMAN J,OLSHEN R,et al.Classification and regression tr ees monterey［M］.Belmont:Wadsworth International Group, 1984.
［4］HELMBOLD D P, LONG P M. Tracking drifting concepts by minimizing disagreem ents［J］. Machine Learning, 1994,21(14):27-45.
［5］WANG H, FAN W, YU P,HAN J.Mining conceptdrifting data streams using ensem bl e classifiers［A］.The 9th ACM International Conference on Knowledge Discove ry and Data Mining (SIGKDD’s03)［C］.New York: ACM Press, 2003.
［6］GANTI V, GEHRKE J, RAMAKRISHNAN R. Mining data streams under block evolut ion［A］. SIGKDD’s02［C］. New York: ACM Press,2002.
［7］WIDMER G,KUBAT M.Learning in the presence of concept drift and hidden contexts［J］. Machine Learning, 1996,23(1):69-101.
［8］DOMINGOS P, HULTEN G. Mining highspeed data streams［A］. In Pro ceeding s o f th e Association for Computing Machinery Sixth International Conference on Knowledg e Discovery and Data Mining［C］. New York: ACM Press, 2000.
［9］PAPADIMITRIOU S, FALOUTSOS C, BROCKWELL A. Adaptive, handsoff stre am mi ning［A］. Proceedings of the 29th International Conference on Very Large Dat a Bases(V LDB’s03)［C］. Berlin:Springer Press, 2003.
［10］AGGARWAL C, HAN J, WANG J, YU P S. On demand classification of data streams ［A］. Proc 2004 Int Conf on Knowledge Discovery and Data Mining［C］. New York: ACM Press, 2004
［11】LAST M. Online classification of nonstationary data streams［J］.Intelli gent Data Analysis,2002,6(2):129-147.
［12］DING Q, DING Q, PERRIZO W. Decision tree classification of spatia l data s tr eams using peano count trees［A］. Proceedings of the ACM Symposium on Applied C omputing［C］. New York: ACM Press, 2002.
［13］GABER M M,KRISHNASWAMY S, ZASLAVSKY A.Onboard mining of data streams in se nsor networks［M］.Springer Verlag,2003
［14］MEHTA M, AGRAWAL R, RISSANEN J. SLIQ: A fast scalable classifier for data m ining［A］. Proc 1996 Int Conf Extending Database Technology (EDBT’s96)［C ］. Springer Press, 1996.
［15］SHAFER J, AGRAWAL R, MEHTA M. SPRINT: A fast scalable parallel classifier f or data mining［A］. Proc 1996 Int Conf Very Large Data Bases (VLDB’s96)［ C］. Springer Press, 1996.
［16］MITCHELL T M.Machine learning［M］.New York:McGrawHill, 1997.
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备注/Memo
收稿日期：2007-03-20.
作者简介：
富春岩，女，1974年生，讲师，主要研究方向为现代数据管理技术、数据流、海量数据处理. E-mail:jmsfu@126.com.
葛茂松，男，1971年生，高级实验师，硕士研究生，主要研究方向为数据挖掘、数据流、海量数据处理.
更新日期/Last Update:
2009-05-07