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Assigning different weights to feature values in naive bayes
- Authors
- Lee, C.-H.
- Issue Date
- 2016
- Publisher
- Springer Verlag
- Keywords
- Feature selection; Feature weighting; Kullback-Leibler; Naive bayes
- Citation
- Communications in Computer and Information Science, v.652, pp 171 - 179
- Pages
- 9
- Indexed
- SCOPUS
- Journal Title
- Communications in Computer and Information Science
- Volume
- 652
- Start Page
- 171
- End Page
- 179
- ISSN
- 1865-0929
- Abstract
- Assigning weights in features has been an important topic in some classification learning algorithms. While the current weighting methods assign a weight to each feature, in this paper, we assign a different weight to the values of each feature. The performance of naive Bayes learning with value-based weighting method is compared with that of some other traditional methods for a number of datasets. © Springer Nature Singapore Pte Ltd. 2016.
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