Improvement of C4.5 decision tree continuous attributes segmentation threshold algorithm and its application

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)

Volumn 42, Issue 12, 2011, Pages 3772-3776

  Yao, Ya Fu ^a   Xing, Liu Tao ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Continuous attribute; Decision tree C4.5 algorithm; Information gain ratio; Segmentation threshold

Indexed keywords

BOUNDARY POINTS; C4.5 ALGORITHM; CLASSIFICATION ACCURACY; CONTINUOUS ATTRIBUTE; IMPROVED ALGORITHM; INFORMATION GAIN RATIO; OPTIMAL SEGMENTATION; SEGMENTATION THRESHOLD; VEHICLE TARGETS; VIDEO SEQUENCES;

DECISION TREES;

ALGORITHMS;

EID: 84857474013     PISSN: 16727207     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Haykin S. Neural networks and learning machines[M]. Beijing: China Machine Press, 2009: 1-7.
2. ZHU Xiao-liang, WANG Jian, YAN Hong-can, et al. Research and application of the improved algorithm C4.5 on decision tree[C]//IEEE International Conference on Test and Measurement, Hongkong, 2009: 184-187.
3. de Toledo P, Rios P M, Ledezma A, et al. Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques[J]. IEEE Trans on Information Technology in Biomedicine, 2009, 13(5): 794-801.
4. Tsang S, Kao B, Yip K Y et al. Decision trees for uncertain data[C]//IEEE International Conference on Data Engineering, Shanghai, 2009: 441-444.
5. LI Fa-chao, LI Ping, JIN Chen-xia. Summary of decision tree algorithm and its application in attribute reduction[R]. Shijiazhuang: Hebei Univ of Sci and Technol, 2009: 313-317.
6. XIE Jin-mei, WANG Yan-ni. Summary of decision tree algorithms[J]. Software Guide 2008, 7(11): 83-85.
7. WANG Tian, SHE Guang-hui. Application of C4.5 based decision tree algorithm about investigation and analysis of forestry resources[J]. Journal of Nanjing Forestry University: Natural Sciences Edition, 2007, 31(3): 115-118.
8. Hunt E B, Marin J, Stone P T. Experiments in induction[M]. San Diego: Academic Press, 1966: 77-89.
9. CHEN Jin, LUO De-Lin, MU Fen-xiang. An improved ID3 decision tree algorithm[C]//4th International Conference on IEEE Computer Science and Education. Chengdu, 2009: 127-130.
10. Quinlan J R. C4.5: Programs for machine learning[M]. San Mateo: Morgan Kaufmann Publishers Inc, 1993: 17-42.
11. YANG Xue-bing, ZHANG Jun. Decision tree algorithm and its key techniques[J]. Computer Technology and Development, 2007, 17(1): 44-46.
12. Roiger R J, Geatz M W. Data mining tutorial[M]. WENG Jing-mong, trans. Beijing: Tsinghua University Press, 2008: 60-85.
13. Maszczyk T, Duch W. Comparison of Shannon, Renyi and Tsallis entropy used in decision trees[J]. Artificial Intelligence and Soft Computing, 2008, 5097: 643-651.
14. Kantardzic M. Data mining: Concepts, models, and algorithms[M]. New York: John Wiley and IEEE Press, 2003: 139-164.
15. ZHOU Chi, XIAO Wei-min, Tirpak T M, et al. Evolving accurate and compact classification rules with gene expression programming[J]. IEEE Transactions on Evolutionary Computation, 2003, 7(6): 519-531.
16. Fayyad U M, Irani K B. On the handling of continuous-value attributes in decision tree generation[J]. Machine Learning, 1992, 8(1): 87-102.