A gradient approach for value weighted classification learning in naive Bayes

Knowledge-Based Systems

Volumn 85, Issue , 2015, Pages 71-79

  Lee, Chang Hwan ^a  

^a Dongguk University   (South Korea)

Author keywords

Bayesian learning; Classification; Feature weighting; Gradient descent

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; GRADIENT METHODS; LEARNING SYSTEMS;

BAYESIAN LEARNING; CLASSIFICATION LEARNING; FEATURE WEIGHTING; GRADIENT APPROACH; GRADIENT DESCENT; NAIVE-BAYES LEARNING; STATE-OF-THE-ART METHODS; WEIGHTING METHODS;

LEARNING ALGORITHMS;

EID: 84937525261     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.04.020     Document Type: Article

References (22)

1. L. Breiman, J. Friedman, R. Olshen, and C. Stone Classification and Regression Trees 1984 Wadsworth and Brooks Monterey, CA
2. Leo Breiman Random forests Mach. Learn. 45 1 2001 5 32
3. Claire Cardie, Nicholas Howe, Improving minority class prediction using case-specific feature weights, in: Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp. 57-65.
4. Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, and Peter L. Bartlett Exponentiated gradient algorithms for conditional random fields and max-margin markov networks J. Mach. Learn. Res. 9 2008 1775 1822
5. Pedro Domingos, and Michael Pazzani On the optimality of the simple bayesian classifier under zero-one loss Mach. Learn. 29 2-3 1997
6. Usama M. Fayyad, Keki B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Int'l Joint Conference on Artificial Intelligence, 1993, pp. 1022-1029.
7. A. Frank, A. Asuncion, UCI Machine Learning Repository, Irvine, CA: University of California, School of Inforaton and Computer Science, 2010. Available from: < http://archive.ics.uci.edu/ml >.
8. David Mandell Freeman, Using naive bayes to detect spammy names in social networks, in: Proceedings of the 2013 ACM workshop on Artificial intelligence and security, 2013, pp. 3-12.
9. Nir Friedman, Dan Geiger, Moises Goldszmidt, G. Provan, P. Langley, and P. Smyth Bayesian network classifiers Mach. Learn. 1997 131 163
10. Thomas Gärtner, Peter A. Flach, Wbcsvm: weighted bayesian classification based on support vector machines, in: The Eighteenth International Conference on Machine Learning, 2001.
11. Mark Hall A decision tree-based attribute weighting filter for naive bayes Knowl.-Based Syst. 20 2 2007
12. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten The weka data mining software: an update SIGKDD Explor. 11 2009
13. Ron Kohavi, Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid, in: Second International Conference on Knoledge Discovery and Data Mining, 1996.
14. Pat Langley, Stephanie Sage, Induction of selective bayesian classifiers, in: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 1994, pp. 399-406.
15. Chang-Hwan Lee An information-theoretic filter method for feature weighting in naive bayes Int. J. Pattern Recogn. Artif. Intell. 28 5 2014
16. Chang-Hwan Lee, Fernando Gutierrez, Dejing Dou, Calculating feature weights in naive bayes with kullback-leibler measure, in: 11th IEEE International Conference on Data Mining, 2011.
17. Vivek Narayanan, Ishan Arora, Arjun Bhatia, Fast and accurate sentiment classification using an enhanced naive bayes model, in: Intelligent Data Engineering and Automated Learning IDEAL 2013, 2013, pp. 194-201.
18. John C. Platt, Sequential minimal optimization: a fast algorithm for training support vector machines, Technical report, Advances in Kernel Methods - Support Vector Learning, 1998.
19. C.A. Ratanamahatana, and D. Gunopulos Feature selection for the naive bayesian classifier using decision trees Appl. Artif. Intell. 17 5-6 2003 475 487
20. Dietrich Wettschereck, David W. Aha, and Takao Mohri A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms Artif. Intell. Rev. 11 1997 273 314
21. Harry Zhang, Shengli Sheng, Learning weighted naive bayes with accurate ranking, in: ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining, 2004.
22. Zijian Zheng, and Geoffrey I. Webb Lazy learning of bayesian rules Machine Learning 2000 Kluwer Academic Publishers 53 84