
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Xingquan Zhu, Xindong Wu, "CostConstrained Data Acquisition for Intelligent Data Preparation," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 11, pp. 15421556, November, 2005.  
BibTex  x  
@article{ 10.1109/TKDE.2005.176, author = {Xingquan Zhu and Xindong Wu}, title = {CostConstrained Data Acquisition for Intelligent Data Preparation}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {17}, number = {11}, issn = {10414347}, year = {2005}, pages = {15421556}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.176}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  CostConstrained Data Acquisition for Intelligent Data Preparation IS  11 SN  10414347 SP1542 EP1556 EPD  15421556 A1  Xingquan Zhu, A1  Xindong Wu, PY  2005 KW  Index Terms Data mining KW  intelligent data preparation KW  data acquisition KW  costsensitive KW  machine learning KW  instance ranking. VL  17 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] M. Berry and G. Linoff, Mastering Data Mining. Wiley, 1999.
[2] J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.
[3] D. Pyle, Data Preparation for Data Mining. Morgan Kauffman, 1999.
[4] T. Redman, Data Quality for the Information Age. Artech House, 1996.
[5] J. Quinlan, “Unknown Attribute Values in Induction,” Proc. Sixth Int'l Conf. Machine Learning Workshop, pp. 164168, 1989.
[6] R. Greiner, A. Grove, and A. Kogan, “Knowing What Doesn't Matter: Exploiting the Omission of Irrelevant Data,” Artificial Intelligence, vol. 97, nos. 12, Dec. 1997.
[7] D. Schuurmans and R. Greiner, Learning to Classify Incomplete Examples, In Computational Learning Theory and Natural Learning Systems: Making Learning Systems Practical. MIT Press, 1996.
[8] N. Friedman, “Learning Belief Networks in the Presence of Missing Values and Hidden Variables,” Proc. Int'l Conf. Machine Learning, pp. 125133, 1997.
[9] L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Wadsworth & Brooks, 1984.
[10] A. Shapiro, Structured Induction in Expert Systems. AddisonWesley, 1987.
[11] R. Little and D. Rubin, Statistical Analysis with Missing Data. New York: Wiley, 1987.
[12] P. Clark and T. Niblett, “The CN2 Induction Algorithm,” Machine Learning, vol. 3, no. 4, pp. 261283, 1989.
[13] I. Kononenko, I. Bratko, and E. Roskar, “Experiments in Automatic Learning of Medical Diagnostic Rules,” technical report, Jozef Stefan Inst., Ljubljana, Yugoslavia, 1984.
[14] S. Tseng, K. Wang, and C. Lee, “A PreProcessing Method to Deal with Missing Values by Integrating Clustering and Regression Techniques,” Applied Artificial Intelligence, vol. 17, nos. 56, pp. 535544, 2003.
[15] J. Quinlan, C4. 5: Programs for Machine Learning. San Mateo, Calif.: Morgan Kaufmann, 1993.
[16] J. Quinlan, “Induction of Decision Trees,” Machine Learning, vol. 1, pp. 81106, 1986.
[17] D. Wilson, “Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,” IEEE Trans. Systems, Man, and Cybernetics, vol. 2, pp. 408421, 1972.
[18] D. Aha, D. Kibler, and M. Albert, “InstanceBased Learning Algorithms,” Machine Learning, vol. 6, no. 1, pp. 3766, 1991.
[19] D. Wilson and T. Martinez, “Reduction Techniques for ExamplarBased Learning Algorithms,” Machine Learning, vol. 38, no. 3, pp. 257268, 2000.
[20] P. Winston, “Learning Structural Descriptions from Examples.” The Psychology of Computer Vision, New York: McGrawHill, 1975.
[21] F. Provost, D. Jensen, and T. Oates, “Efficient Progressive Sampling,” Proc. Fifth ACM SIGKDD, pp. 2332, 1999.
[22] D. Lewis and J. Catlett, “Heterogeneous Uncertainty Sampling for Supervised Learning,” Proc. 11th Int'l Conf. Machine Learning, pp. 148156, 1994.
[23] D. Cohn, L. Atlas, and R. Ladner, “Improving Generalization with Active Learning,” Machine Learning, vol. 15, pp. 201221, 1994.
[24] H. Seung, M. Opper, and H. Sompolinsky, “Query by Committee,” Proc. ACM Workshop Computational Learning Theory, 1992.
[25] D. Mackay, “InformationBased Objective Functions for Active Data Selection,” Neural Computation, vol. 4, no. 4, pp. 590604, 1992.
[26] D. Lewis and W. Gale, “A Sequential Algorithm for Training Text Classifiers,” Proc. Int'l SIGIR Conf. Research and Development in Information Retrieval, pp. 312, 1994.
[27] Z. Zheng and B. Padmanabhan, “On Active Learning for Data Acquisition,” Proc. IEEE Conf. Data Mining, pp. 562569, 2002.
[28] X. Zhu, X. Wu, and Y. Yang, “Error Detection and ImpactSensitive Instance Ranking in Noisy Data Set,” Proc. 19th Nat'l Conf. Artificial Intelligence (AAAI), 2004.
[29] X. Zhu and X. Wu, “Data Acquisition with Active and ImpactSensitive Instance Selection,” Proc. IEEE Int'l Conf. Tools with Artificial Intelligence (ICTAI), 2004.
[30] D. Lizotte, O. Madani, and R. Greiner, “Budgeted Learning of NaiveBayes Classifiers,” Proc. Uncertainty in Artificial Intelligence, 2003.
[31] P. Turney, “CostSensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm,” J. Artificial Intelligence Research, vol. 2, pp. 369409, 1995.
[32] C. Blake and C. Merz, UCI Repository of Machine Learning Databases, 1998.
[33] M. Nunez, “The Use of Background Knowledge in Decision Tree Induction,” Machine Learning, vol. 6, pp. 231250, 1991.
[34] M. Tan, “, CostSensitive Learning of Classification Knowledge and Applications in Robotics,” Machine Learning, vol. 13, 1993.
[35] P. Hoel, “Likelihood Ratio Tests,” Introduction to Mathematical Statistics, third ed. New York: Wiley, 1962.
[36] C. Shannon and W. Warren, The Mathematical Theory of Communication. Univ. of Illinois Press, 1971.
[37] A. Freitas, “Understanding the Crucial Role of Attribute Interaction in Data Mining,” Artificial Intelligence Rev., vol. 16, no. 3, pp. 177199, 2001.
[38] A.K. Jain and R.C. Dubes, Algorithms for Clustering Data. Prentice Hall, 1998.