The Irrelevant Values Problem of Decision Tree in Medical Examination

Kuang-Yi Chou³, Huan-Chao Keh¹, Nan-Ching Huang This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Shing-Hwa Lu², Ding-An Chiang¹ and Yuan-Cheng Cheng¹

¹Department of CSIE, Tamkang University, Tamsui, Taiwan 251, R.O.C.
²Department of Urology, School of Medicine, National Yang-Ming University and Department of Urology, Zhong Xiao Branch, Taipei City Hospital, Urological Center, Taipei, Taiwan 115, R.O.C.
³Center of General Education, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan 112, R.O.C.

REFERENCES

1. [1] Jerez-Aragonés, J. M., et al., “A Combined Neural Network and Decision Trees Model for Prognosis of Breast Cancer Relapse,” Artificial Intelligence in Medicine, Vol. 27, pp. 45
   63 (2003).
2. [2] Kononenko, I., “Machine Learning for Medical Diagnosis: History, State of the Art and Perspective,” Artificial Intelligence in Medicine, Vol. 23, pp. 89
   109 (2001).
3. [3] Neumann, A., et al., “Machine Learning for Medical Diagnosis: History, State of the Art and Perspective,” Artificial Intelligence in Medicine, Vol. 32, pp. 97
   113 (2004).
4. [4] Cheng, J., et al., “Improved Decision Trees: A Generalized Version of ID3,” in Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, Michigan, USA, pp. 100
   108 (1988).
5. [5] Bohanec, M. and Rajkovic, V., “DEX: An Expert System Shell for Decision Support,” Sistemica, Vol. 1, pp. 145
   157 (1990).
6. [6] Delen, D., et al., “Predicting Breast Cancer Survivability: A Comparison of Three Data Mining Methods,” Artificial Intelligence in Medicine, Vol. 34, pp. 113
   127 (2005).
7. [7] Fayyad, U. M., “Branching on Attribute Values in Decision Tree Generalization,” in Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, Washington, pp. 601
   606 (1994).
8. [8] Fayyad, U. M. and Irani, K. B., “A Machine Learning Algorithm (GID3*) for Automated Knowledge Acquisition: Improvements and Extensions,” MI: GM Research Labs, Warren (1991).
9. [9] John, G. H., et al., “Irrelevant Features and the Subset Selection Problem,” in Machine Learning: Proceedings of the Eleventh International Conference, W. W. Cohen and H. Hirsh, Eds., ed San Fransisco, CA: Morgan Kaufmann Publisher, pp. 121
   129 (1994).
10. [10] Maher, P. E. and St. Clair, D., “Uncertain Reasoning in an ID3 Machine Learning Framework,” in Proceedings of the Second IEEE International Conference on Fuzzy Systems, pp. 7
    12 (1993).
11. [11] Breiman, L., et al., Classification and regression trees, Belmont, California: Wadsworth (1984).
12. [12] Chiang, D.-A., et al., “The Irrelevant Values Problem in the ID3 Tree,” Computers and Artificial Intelligence, Vol. 19, pp. 169
    182 (2000).
13. [13] Cai, Y., et al., “An Attribute-Oriented Approach for Learning Classification Rules from Relational Databases,” in Proceedings of the Sixth International Conference on Data Engineering, Los Angeles, California, USA, pp. 281
    288 (1990).
14. [14] Chi, Z. and Yan, H., “ID3-Derived Fuzzy Rules and Optimized Defuzzification for Handwritten Numeral Recognition,” Fuzzy Systems, IEEE Transactions on, Vol. 4, pp. 24
    31 (1996).
15. [15] Kamber, M., et al., “Generalization and Decision Tree Induction: Efficient Classification in Data Mining,” in Proceedings of the Seventh International Workshop on Research Issues in Data Engineering, pp. 111
    120 (1997).
16. [16] Ou, M. H., et al., “Dynamic Knowledge Validation and Verification for CBR Teledermatology System,” Artificial Intelligence in Medicine, Vol. 39, pp. 79
    96 (2007).