Inference-based naïve bayes: Turning naïve bayes cost-sensitive

IEEE Transactions on Knowledge and Data Engineering

Volumn 25, Issue 10, 2013, Pages 2302-2313

  Fang, Xiao ^a  

^a UNIVERSITY OF UTAH   (United States)

Author keywords

classification; Cost sensitive classification; Na ve Bayes

Indexed keywords

BAYES METHOD; BIASED ESTIMATION; COST-SENSITIVE; COST-SENSITIVE CLASSIFICATION; ORDER RELATION; REAL-WORLD; TRAINING DATA;

CLASSIFICATION (OF INFORMATION); PROBABILITY;

COSTS;

EID: 84883299058     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2012.196     Document Type: Article

References (53)

1. E.L. Allwein, R.E. Schapire, and Y. Singer, "Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers," J. Machine Learning Research, vol. 1, pp. 113-141, 2000. (Pubitemid 33738778)
2. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. Chapman and Hall, 1984.
3. L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, pp. 123-140, 1996. (Pubitemid 126724382)
4. T.M. Cover and P.E. Hart, "Nearest Neighbor Pattern Classification," IEEE Trans. Information Theory, vol. IT-13, no. 1, pp. 21-27, Jan. 1967.
5. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, "SMOTE: Synthetic Minority Over-Sampling Technique," J. Artificial Intelligence Research, vol. 16, pp. 321-357, 2002. (Pubitemid 43057176)
6. P. Domingos and M. Pazzani, "On the Optimality of the Simple Bayesian Classifier Under Zero-One Loss," Machine Learning, vol. 29, pp. 103-130, 1997. (Pubitemid 127510035)
7. P. Domingos, "MetaCost: A General Method for Making Classifiers Cost-Sensitive," Proc. Fifth Int'l Conf. Knowledge Discovery Data Mining, pp. 155-164, 1999.
8. C. Drummond and R.C. Holte, "C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling Beats Over-Sampling," Proc. Workshop Learning Imbalanced Datasets (ICML'03), 2003.
9. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. John Wiley, 1973.
10. C. Elkan, "The Foundation of Cost-Sensitive Learning," Proc. 17th Int'l Joint Conf. Artificial Intelligence, pp. 973-978, 2001.
11. W. Fan, S.J. Stolfo, J. Zhang, and P. Chan, "AdaCost: Misclassification Cost-Sensitive Boosting" Proc. 17th Int'l Conf. Machine Learning, pp. 97-105, 1999.
12. A. Frank and A. Asuncion, UCI Machine Learning Repository, School of Information and Computer Science, Univ. of California, http://archive.ics.uci. edu/ml, 2010.
13. Y. Freund and R.E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," J. Computer System Sciences, vol. 55, no. 1, pp. 119-139, 1997. (Pubitemid 127433398)
14. J.H. Friedman, "On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality," Data Mining Knowledge Discovery, vol. 1, pp. 55-77, 1997. (Pubitemid 127721236)
15. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian Network Classifiers," Machine Learning, vol. 29, nos. 2/3, pp. 131-163, 1997. (Pubitemid 127510036)
16. E. Frank, M. Hall, and B. Pfahringer, "Locally Weighted Naive Bayes," Proc. Conf. Uncertainty Artificial Intelligence, pp. 249-256, 2003.
17. J. Gama, "Iterative Bayes," Intelligent Data Analysis, vol. 4, pp. 475-488, 2000.
18. A.K. Ghosh, "On Optimum Choice of k in Nearest Neighbor Classification," Computational Statistics Data Analysis, vol. 50, no. 11, pp. 3113-3123, 2006. (Pubitemid 43729105)
19. P. Hall, B.U. Park, and R. Samworth, "Choice of Neighbor Order in Nearest-Neighbor Classification," Annals Statistics, vol. 36, no. 5, pp. 2135-2152, 2008.
20. J. Han and M. Kamber, Data Mining: Concepts and Techniques, second ed. Morgan Kaufmann, 2005.
21. D.J. Hand and W.E. Henley, "Statistical Classification Methods in Consumer Credit Scoring: A Review," J. Royal Statistical Soc.: Series A, vol. 160, no. 3, pp. 523-541, 1997.
22. D.J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining. MIT Press, 2001.
23. T. Hastie and R. Tibshirani, "Classification by Pairwise Coupling," Annals Statistics, vol. 26, no. 2, pp. 451-471, 1998.
24. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer, 2001.
25. L. Jiang, H. Zhang, and Z. Cai, "A Novel Bayes Model: Hidden näve Bayes," IEEE Trans. Knowledge Data Eng., vol. 21, no. 10, pp. 1361-1371, Oct. 2009.
26. M. Kubat and S. Matwin, "Addressing the Curse of Imbalanced Training Sets: One-Sided Selection," Proc. 14th Int'l Conf. Machine Learning, pp. 179-186, 1997.
27. A. Lemmens and C. Croux, "Bagging and Boosting Classification Trees to Predict Churn," J. Marketing Research, vol. 43, no. 2, pp. 276-286, 2006. (Pubitemid 43813059)
28. C.X. Ling and C. Li, "Data Mining for Direct Marketing: Problems and Solutions," Proc. Fourth Int'l Conf. Knowledge Discovery Data Mining, pp. 73-79, 1998.
29. X.-Y. Liu and Z.-H. Zhou, "Learning with Cost Intervals," Proc. 16th Int'l Conf. Knowledge Discovery Data Mining, pp. 403-412, 2010.
30. R. Lippmann, "Pattern Classification Using Neural Networks," IEEE Commun. Magazine, vol. CM-27, no. 11, pp. 47-64, Nov. 1989.
31. D. Margineantu, "Class Probability Estimation and Cost-Sensitive Classification Decisions," Proc. 13th European Conf. Machine Learning, pp. 270-281, 2002.
32. T.M. Mitchell, Machine Learning. McGraw Hill, http://www.cs.cmu.edu/tom/ NewChapters.html, 1997.
33. M. Pazzani, C. Merz, P. Murphy, K. Ali, T. Hume, and C. Brunk, "Reducing Misclassification Costs," Proc. 11th Int'l Conf. Machine Learning (ICML '94), pp. 217-225, 1994.
34. J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
35. A.K. Seewald, "An Evaluation of näve Bayes Variants in Content-Based Learning for Spam Filtering," Intelligent Data Analysis, vol. 11, pp. 497-524, 2007.
36. V.S. Sheng and C.X. Ling, "Thresholding for Making Classifiers Cost-Sensitive," Proc. 21st Nat'l Conf. Artificial Intelligence, pp. 476-481, 2006. (Pubitemid 44705329)
37. K.M. Ting, "A Comparative Study of Cost-Sensitive Boosting Algorithms," Proc. 17th Int'l Conf. Machine Learning (ICML '00), pp. 983-990, 2000.
38. K.M. Ting, "An Empirical Study of MetaCost using Boosting Algorithms," Proc. 11th European Conf. Machine Learning (ECML '00), pp. 413-425, 2000.
39. K.M. Ting, "An Instance-Weighting Method to Induce Cost-Sensitive Trees," IEEE Trans. Knowledge and Data Eng., vol. 14, no. 3, pp. 659-665, May/June 2002. (Pubitemid 34669622)
40. V. Vapnik, Statistical Learning Theory. John Wiley, 1998.
41. G.I. Webb, J.R. Boughton, and Z. Wang, "Averaged One-Dependence Estimators: Preliminary Results," Proc. First Australasian Data Mining Workshop, pp. 65-73, 2002.
42. W.H. Wolberg and O.L. Mangasarian, "Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology," Proc. Nat'l Academy of Sciences USA, vol. 87, no. 23, pp. 9193-9196, 1990. (Pubitemid 120030591)
43. B. Zadrozny and C. Elkan, "Learning and Making Decisions When Costs and Probabilities are Both Unknown," Proc. Seventh Int'l Conf. Knowledge Discovery Data Mining (KDD '01), pp. 204-213, 2001.
44. H. Zhao, "Instance Weighting versus Threshold Adjusting for Cost-Sensitive Classification," Knowledge Information Systems, vol. 15, pp. 321-334, 2008.
45. Z. Zheng, "näve Bayesian Classifier Committees," Proc. 10th European Conf. Machine Learning (ECML '98), pp. 196-207, 1998.
46. Z.-H. Zhou and X.-Y. Liu, "Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 1, pp. 63-77, Jan. 2006. (Pubitemid 43145089)
47. J. Demsar, "Statistical Comparisons of Classifiers over Multiple Data Sets," J. Machine Learning Research, vol. 7, pp. 1-30, 2006. (Pubitemid 43022939)
48. Y. Kim, N. Street, G. Russell, and F. Menczer, "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, vol. 51, no. 2, pp. 264-276, 2005. (Pubitemid 40360167)
49. G.I. Webb, J.R. Boughton, and Z. Wang, "Not So näve Bayes: Aggregating One-Dependence Estimators," Machine Learning, vol. 58, pp. 5-24, 2005. (Pubitemid 40356736)
50. F. Zheng and G.I. Webb, "Semi-näve Bayesian Classification," Monash Univ. Working Paper, 2008.
51. G.I. Webb, J.R. Boughton, F. Zheng, K.M. Ting, and H. Salem, "Decreasingly näve Bayes: Aggregating n-Dependence Estimators," Monash Univ. Working Paper, 2010.
52. H. Zhang and S. Sheng, "Learning Weighted Naive Bayes with Accurate Ranking," Proc. IEEE Fourth Int'l Conf. Data Mining (ICDM '04), pp. 567-570, 2004. (Pubitemid 40731109)
53. M. Hall, "A Decision Tree-Based Attribute Weighting Filter for näve Bayes," Knowledge-Based Systems, vol. 20, no. 2, pp. 120-126, 2007. (Pubitemid 46282455)