Artificial immune system for attribute weighted Naive Bayes classification

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
The 2013 International Joint Conference on Neural Networks, 2013, pp. 1 - 8
Issue Date:
2013-01
Full metadata record
Files in This Item:
Filename Description Size
Artificial Immune System.pdfAccepted manuscript437.22 kB
Adobe PDF
Naive Bayes (NB) is a popularly used classification method. One potential weakness of NB is the strong conditional independence assumption between attributes, which may deteriorate the classification accuracy. In this paper, we propose a new Artificial Immune System based Weighted Naive Bayes (AISWNB) classifier. AISWNB uses immunity theory in artificial immune systems to find optimal weight values for each attribute. The adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. Because AISWNB uses artificial immune system search mechanism to find optimal weights, it does not need to know the importance of individual attributes nor the relevance among attributes. As a result, it can obtain optimal weight value for each attribute during the learning process. Experiments and comparisons on 36 benchmark data sets demonstrate that AISWNB outperforms other state-of-the-art attribute weighted NB algorithms.
Please use this identifier to cite or link to this item: