Research and application of the improved algorithm C4.5 on decision tree

Proceedings of the International Symposium on Test and Measurement

Volumn 2, Issue , 2009, Pages 184-187

  Zhu, Xiaoliang ^a   Wang, Jian ^a   Yan, Hongcan ^a   Wu, Shangzhuo ^a  

^a HEBEI POLYTECHNIC UNIVERSITY   (China)

Author keywords

C4.5 algorithm; Decision tree; Pruning; Training data

Indexed keywords

BASIC CONCEPTS; C4.5 ALGORITHM; HIGH EFFICIENCY; IMPROVED ALGORITHM; INDUCTIVE INFERENCE; PREDICTION MODEL; RESEARCH AND APPLICATION; SIMPLE METHOD; TRAINING DATA;

ALGORITHMS; CLASSIFIERS; DECISION TREES; KNOWLEDGE REPRESENTATION; MATHEMATICAL MODELS;

INFERENCE ENGINES;

EID: 77951199871     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTM.2009.5413078     Document Type: Conference Paper

References (23)

1. Feng Shaorong:Research and Improvement of DecisionTreesAlgorithm. Journalof XiamenUniversity (NaturalScience), 2007,pp.498-500.
2. Mehmed Kantardzicl:Data Mining-Concept,model,Methods and development.
3. Zhang Jun ,Yang Xuebing: Decision Tree and Its Key Techniques.Computer Technology and Development.2006, pp.44-46
4. Sun Chaili: Study on Data Stream M in ing Algorithm Based on Decision TreeJournal of Taiyuan University of Science and Technology.,2006, pp.269-270.
5. Zhang Dezheng,Li Tianchi,Aziguli:Application of Decision Tree Produced by C4.5 on Natural Gas Data for Analysing Measuring Error2006, pp..208-210.
6. Mao Guojun,Duan Lijuan,Wang Shi: Principles of Data Mining and Algorithms. TsinghuaUniversity Press,Beijing.2006.
7. Gui Xiancai,Peng Hong,Wang Xiaohua:Application of C4.5 Algorithm in the Analysis of Customer Chum Insurance. Computer Engineering and Applications.2006, pp.197-199.
8. Yang Xuebiang,Zhangjun: Decision Tree and Its Key Techniques. ComputerTechnology and Development.2007, pp.43-45.
9. QuinlanJ R.C4.5:Programs for MachineLearning[M].[s.1.],1996.
10. Quinlan J R.Induction of Decision Tree[J].Maching Learning, 1986(1): pp.81-106.
11. Mehta M , Agrawal R , Rissanen J . SLIQ : A Fast and Scalable Classifier for Data Mining [M].US:IBM Almaden Research Center,1996.
12. Shafer J C , Agrawal R , Mehta M. SPRINT : A Scalable Parallel Classifierfor Data Mining[C]/ / Proc of the 22ndInt Confon VeryLarge Databases. Mumbai(Bombay), India: [s. n. ] ,1996.
13. Rastogi R,Shim K.PUBLIC:A Decision Tree Classifier that Integrates Buildingand Pruning[R].Murray Hill :BellLaboratories ,1998.
14. WangH, FanW, Yu P S, Han J. Mining ConceptDriftingData Streams using Ensemble Classifiers[ C ]. Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and DataMining , Washington, D. C. , Aug. 2003.
15. HulenG, SpencerL, Domingos P. MiningTime2Changing Data Streams [ C ]. Proc. of the Tth ACM SIGKDD Int. Conf. on Knowledge Discovery and DataMining. SanFrancisco, 2001.
16. Breiman L,Friedman J h,OlshenR A,et al.Classification and regression trees [M].California:WadsworthPublishing Company, 1984.
17. Quinlan J R.Induction of decision tree [J]. MachineLearning ,1986 ,1 (1) : 81-106.
18. Freund Y. Boosting a weak learning algorithm by majority [J].Information and Computation ,1995,121(2),pp.256-285
19. BreimanL. Baggingpredictors [J]..Machine LearningJournal ,1996 ,24 (2) pp.123- 140.
20. Quinlan,J. R. C4.5: Programs for MachineLearning. Morgan Kaufmann Publishers, 1993.
21. S.B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Informatica 31,2007,pp. 249-326
22. 1. R. Quinlan. Improveduse of continuous attributes in c4.5. Journal of Artificial Intelligence Research, 4, 1996,pp.77-90
23. Y. Yuan and MJ. Shaw, Induction of fuzzy decision trees. Fuzzy Sets and Systems69 (1995),pp. 125-139