Browse > Article

Calculating the Importance of Attributes in Naive Bayesian Classification Learning  

Lee, Chang-Hwan (Department of Information and Communications, Dongguk University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.48, no.5, 2011 , pp. 83-87 More about this Journal
Abstract
Naive Bayesian learning has been widely used in machine learning. However, in traditional naive Bayesian learning, we make two assumptions: (1) each attribute is independent of each other (2) each attribute has same importance in terms of learning. However, in reality, not all attributes are the same with respect to their importance. In this paper, we propose a new paradigm of calculating the importance of attributes for naive Bayesian learning. The performance of the proposed methods has been compared with those of other methods including SBC and general naive Bayesian. The proposed method shows better performance in most cases.
Keywords
artificial intelligence; data mining; machine learning; naive Bayesian; classification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Frank and A. Asuncion UCI repository of machine learning databases. 2011.
2 Pat Langley and Stephanie Sage. Induction of selective bayesian classifiers. In the Tenth Conference on Uncertainty in Artificial Intelligence, pages 399-406, 1994.
3 J. Ross Quinlan. C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
4 Dietrich Wettschereck, David W. Aha, and Takao Mohri. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11:273-314, 1997.   DOI   ScienceOn
5 Claire Cardie and Nicholas Howe. Improving minority class prediction using case-specific feature weights. In the Fourteenth International Conference on Machine Learning, pages 57-65, 1997.
6 Pedro Domingos and Michael Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2-3), 1997.
7 Thomas Gartner and Peter A. Flach. Wbcsvm: Weighted bayesian classification based on support vector machines. In the Eighteenth International Conference on Machine Learning, 2001.
8 Mark Hall. A decision tree-based attribute weighting filter for naive bayes. Knowledge-Based Systems, 20(2), 2007. 13
9 S. Kullback and R. A. Leibler. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79-86, 1951.   DOI   ScienceOn