A sample selection algorithm in fuzzy decision tree induction and its theoretical analyses

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2007, Pages 3621-3626

  Wang, Xi Zhao ^a,b   Yan, Jian Hui ^b   Wang, Ran ^c   Dong, Chun Ru ^b  

^a IEEE

^b HEBEI UNIVERSITY   (China)

^c BEIJING FORESTRY UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; FUZZY CONTROL; REDUNDANCY;

DATASETS; FUZZY DECISION TREE; THEORETICAL ANALYSES;

DECISION TREES;

EID: 40949137311     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2007.4413726     Document Type: Conference Paper

References (19)

1. Norbert Jankowski, Marek Grochowski. Comparison of Instances Selection Algorithms. ICAISC 2004, 2004, LNAI 3070;598-603
2. David W. Aha, Dennis Kibler, Marck Albert. Instance-Based Learning Algorithms. Machine Learning, 1991, 6:37-66
3. Guo Mao-Zu, Su Xiao-Hong and Wang Ya-Dong. Research of Index and Retrieval Mechanism Based on IBL Algorithm in CBR System. Computer engineering and applications. 2001,5:67-69
4. Quinlan J R. Induction of decision tree, Machine Learning, 1986, 1: 81-106
5. Yufei Yuan, Michael J Shaw. Induction of fuzzy decision trecs[J].Fuzzy Sets and System, 1995;69(2):125-139
6. Utgoff P E. Incremental induction of decision trees [J]. Machine Learning, 1989,(4): 161-186
7. Zhang Pei-Jian and Xu Hua. Study and Application of Active Learning with SVM. Computer Applications, 2004,24:1-3
8. John Durkin, Cai Jingfeng, Cai Zixing. Decision tree and technique and its current research. Control engineering of china. 2005,12(1): 15-18.
9. Lewis D D, Gale W A. A Sequential algorithm for training text classifiers. In: Proc of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval Springer Verlag, 1994. 3-12.
10. Cohn, D.A., Ghahramani, Z., & Jordan, M.I. Active learning with statistical models. Journal of Artificial Intelligence research, 1996(4):129-145
11. Seung, H. S., Opper, M., & Sompolinsky, H. Query by committee. Computational Learning Theory, 1992:441-448
12. Wang Xi-Zhao, Ling Wei-De. A comparison of two heuristics for fuzzy decision tree generation [J]. Journal of Hebei University, 2000;20(1):1-6.
13. Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14(1968) 515-516
14. Ritter, G.L., Woodruff, H.B., Lowry, S.R., Isenhour, T.L.: An algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory 21(1975) 65-669
15. Gates, G.: The reduced nearest neighbor rule. IEEE Transactions on Information Theory 18(1972) 31-433
16. Wilson, D.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2(1972) 408-421
17. Bhattacharya, B.K., Poulsen, R.S., Toussaint, G.T.: Application of proximity graphs to editing nearest neighbor decision rule. In: International Symposium on Information Theory, Santa Monica (1981)
18. Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6 (2002) 153-172
19. Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: International Conference on Machine Learning (1994) 293-301