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A gradient approach for value weighted classification learning in naive Bayes.
- Source :
-
Knowledge-Based Systems . Sep2015, Vol. 85, p71-79. 9p. - Publication Year :
- 2015
-
Abstract
- Feature weighting has been an important topic in classification learning algorithms. In this paper, we propose a new paradigm of assigning weights in classification learning, called value weighting method. While the current weighting methods assign a weight to each feature, we assign a different weight to the values of each feature. The proposed method is implemented in the context of naive Bayesian learning, and optimal weights of feature values are calculated using a gradient approach. The performance of naive Bayes learning with value weighting method is compared with that of other state-of-the-art methods for a number of datasets. The experimental results show that the value weighting method could improve the performance of naive Bayes significantly. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 85
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 108454381
- Full Text :
- https://doi.org/10.1016/j.knosys.2015.04.020