A new pruning method for decision tree based on structural risk of leaf node

Neural Computing and Applications

Volumn 22, Issue SUPPL.1, 2013, Pages 17-26

  Luo, Linkai ^a   Zhang, Xiaodong ^a   Peng, Hong ^a   Lv, Weihang ^a   Zhang, Yan ^a  

^a XIAMEN UNIVERSITY   (China)

Author keywords

Decision tree; Pruning; Structural risk; Volume of leaf node

Indexed keywords

BENCHMARK DATASETS; COST-COMPLEXITY PRUNING; CROSS VALIDATION; GENERALIZATION PERFORMANCE; PRUNING; PRUNING ALGORITHMS; STRUCTURAL RISKS; VOLUME OF LEAF NODE;

ALGORITHMS; DECISION TREES;

DATA MINING;

EID: 84878012828     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1055-6     Document Type: Article

References (15)

1. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth International, Belmont.
2. Zhong M, Georgiopoulos M, Anagnostopoulos G (2008) A k-norm pruning algorithm for decision tree classifiers based on error rate estimation. Mach Learn 71(1): 55-88.
3. Chandra B, Kothari R, Paul P (2010) A new node splitting measure for decision tree construction. Pattern Recogn 43(8): 2725-2731.
4. Breiman L (1996) Technical note: some properties of splitting criteria. Mach Learn 24(1): 41-47.
5. Bohanec M, Bratko I (1994) Trading accuracy for simplicity in decision trees. Mach Learn 15(3): 223-250.
6. Esposito F, Malerba D, Semeraro G (1997) A comparative analysis of methods for pruning decision trees. IEEE Trans Pattern Anal Mach Intell 19(5): 476-491.
7. Leonard A, Wavid W (1997) Simplifying decision trees: a survey. Knowl Eng Rev 12(1): 1-40.
8. Mingers J (1989) An empirical comparison of pruning methods for decision tree induction. Mach Learn 4(2): 227-243.
9. Windeatt T, Ardeshir G (2001) An empirical comparison of pruning methods for ensemble classifiers. In: Proceedings of the 4th international conference on advances in intelligent data analysis, pp 208-217.
10. Niblett T, Bratko I (1986) Learning decision rules in noisy domains. In: Proceedings of expert systems'86. Cambridge University Press, New York, pp 25-34.
11. Quinlan J (1987) Simplifying decision trees. Int J Man Mach Stud 27(3): 221-234.
12. Kim J, Kim Y (2006) Maximum a posteriori pruning on decision trees and its application to bootstrap BUMPing. Comput Stat Data Anal 50(3): 710-719.
13. Richard O, Peter E, David G (2000) Pattern classification, 2nd edn. Wiley, New York.
14. Buntine W, Niblett T (1992) A further comparison of splitting rules for decision-tree Induction. Mach Learn 8(1): 75-85.
15. Blake C, Merz C (1998) UCI repository of machine learning databases. Dept. of Information and Computer Sciences, University of California, Irvine. http://www. ics. uci. edu/~mlearn/MLRepository. html.