Learning k-nearest neighbor naive bayes for ranking

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3584 LNAI, Issue , 2005, Pages 175-185

  Jiang, Liangxiao ^a   Zhang, Harry ^b   Su, Jiang ^b  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; PROBABILITY;

INSTANCE CLONING LOCAL NAIVE BAYES (SIMPLY ICLNB) MODEL; K-NEAREST NEIGHBOR (KNN); KNNDW (KNN WITH DISTANCE WEIGHTED); NAIVE BAYES; NBTREE; RANKING; RECEIVER OPERATING CHARACTERISTICS CURVE);

DATA MINING;

EID: 26944489952     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11527503_21     Document Type: Conference Paper

References (14)

1. Aha, David W., Dennis Kibler, Marc K. Albert. 1991. Instance-Based Learning Algorithms. Machine Learning, vol. 6, pp. 37-66.
2. http://prdownloads.sourceforge.net/weka/datasets-UCI.jar
3. Provost, F. J., Domingos, P.: Tree Induction for Probability-Based Ranking. Machine Learning 52(3) (2003) 199-215
4. Kohavi, R.: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press (1996) 202-207
5. Frank, E., Hall, M., Pfahringer, B.: Locally Weighted Naive Bayes. Proceedings of the Conference on Uncertainty in Artificial Intelligence (2003). Morgan Kaufmann(2003), 249-256.
6. Provost, F., Fawcett, T.: Analysis and visualization of classifier performance: comparison under imprecise class and cost distribution. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining. AAAI Press (1997) 43-48
7. Hand, D. J., Till, R. J.: A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning 45 (2001) 171-186
8. Ling, C. X., Yan, R. J.: Decision Tree with Better Ranking. Proceedings of the 20th International Conference on Machine Learning. Morgan Kaufmann (2003) 480-487
9. Huang, J., Lu, J., Ling, C., X.: Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy. Proceedings of the Third IEEE International Conference on Data Mining. IEEE Computer Society Press(2003), 553-556.
10. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann: San Mateo, CA (1993)
11. Zhang, H., Su, J.: Naive Bayesian Classifiers for Ranking. Proceeding of ECML 2004. Springer (2004) 501-512
12. Xie, Z., Hsu, W., Liu, Z., Lee, M.: SNNB: A Selective Neighborhood Based Naive Bayes for Lazy Learning. Proceedings of the Sixth Pacific-Asia Conference on KDD. Springer (2002) 104-114
13. Zheng, Z., Webb, G. I.,: Lazy Learning of Bayesian Rules. Machine Learning, 41(1) (2000) 53-84
14. Witten, I. H., Frank, E.: Data Mining - Practical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann (2000)