Reference: | 1. Allison, P. D. (2001). Missing data. Thousand Oaks, CA: Sage.
2. Alpaydın, E. (2010). Introduction to machine learning. London, England: The MIT Press.
3. Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464–469.
4. Giks, Walter R ; Richardson, Sylvia; Spiegelhalter, David J. (1996). Introducing Markov chain Monte Carlo. In Markov chain Monte Carlo in practice (pp. 1-19). London: Chapman & hall/CRC.
5. Graham, J. W. (2003). Adding missing-data-relevant variables to FIML basedstructural equation models. Structural Equation Modeling, pp. 10, 80–100.
6. Jackson, J. (2002). Overview, data mining: a conceptual. Communications of the Association for Information Systems.
7. Kohavi, R. (1995). A study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI, (Vol.14, No.2, pp. 1137-1145).
8. Levin, N., & Zahav, J. (2001, Spring). Predictive modeling using segmentation. Journal of Interactive Marketing, 15(2), 2-22.
9. Melville, P., Saar-Tsechansky, M., Provost, F., & Mooney, R. (2004). Active Feature-Value Acquisition for Classifier Induction. Proceedings of the 4th IEEE International Conference on Data Mining, (pp. 483-486). Brighton, UK.
10. Pallant, J. (2007). SPSS survival manual (3rd ed.). New York, NY: Open University Press.
11. Pedro J. Garcı´a-Laencina Æ Jose´-Luis Sancho-Go´mez Æ, A. R.-V. (2010). Pattern classification with missing data: a review. Neural Comput & Applic.
12. Peng, C. Y. J., Harwell, M., Liou, S.M., & Ehman, L.H. (2006). Advances in missing data methods and implications for educational research. In Real data analysis, 31-78. North Carolina,US : Information Age Publishing.
13. Quinlan, J. R. (1989). Unknown attribute values in induction., In ML (pp. 164-168).
14. Rubin, D. B. (1987). Multiple imputation for non-response in surveys. New York: John Wiley & Sons.
15. Saar-Tsechansky, M., Melville, P., & Provost, F. (2009, 4). Active Feature-Value Acqusition. Management Science, 55(4), 664-684.
16. Schafer, J. L. (1999). Multiple imputation: a primer. Statiscal methods in medical research, 8(1), 3-15.
17. Schlomer, G. L., Bauman, S., & Card, N. A. (2010). Best Practices for Missing Data Management in Counseling Psychology. Journal of Counseling Psychology, 57(1), 1-10.
18. Simon, H. A., & Lea, G. (1974). Problem solving and rule induction: A unified view. Knowledge and cognition. Oxford, England: Lawrence Erlbaum.
19. Simon, H., & Lea, G. (1974). Problem solving and rule induction: A unified view.
20. Turney, P. (2000). Types of Cost in Inductive Concept Learning. Proceedings of the Cost-Sensitive Learning Workshop at the 17th ICML-2000 Conference. Stanford, CA.
21. Vinod, N. C., & Punithavalli, D. M. (2011). Classification of Incomplete Data Handling Techniques-An Overview. International Journal on Computer Science and Engineering, 3(1), 340-344.
22. Zheng, Z., & Padmanabhan, B. (2002). On Active Learning for Data Acquisition. Proceedings of IEEE International Condference on Data Mining, (pp. 562-569).
網路資料
1. UCI machine Learning Repository. (n.d.). Retrieved from http://archive.ics.uci.edu/ml/ |