SEIR Immune Strategy for Instance Weighted Naive Bayes Classification
Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods.
KeywordsNaive bayes Classification Immune strategy SEIR
We thank the Australian Research Council (ARC) Discovery Project under Grant No. DP140101366, Shanghai Education Commission under grant No. 14ZS085 and Education Ministry of China under grant No. 12YJA630158, support this work.
- 1.Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)Google Scholar
- 2.Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
- 4.Jiang, L., Cai, Z., Wang, D.: Learning averaged one-dependence estimators by instance weighting. J. Comput. Inf. Syst. 4, 2753–2760 (2008)Google Scholar
- 5.Jiang, L., Zhang, H.: Learning instance greedily cloning naive bayes for ranking. In: Proceedings of ICDM, pp. 202–209 (2005)Google Scholar
- 8.Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers:a decision-tree hybrid. In: Proceedings of KDD, pp. 202–207 (1996)Google Scholar
- 9.Langley, P., Sage, S.: Induction of selective bayesian classifiers. In: Proceedings of UAI, pp. 339–406 (1994)Google Scholar
- 10.Naderpour, M., Lu, J., Zhang, G.: A fuzzy dynamic bayesian network-based situation assessment approach. In: Proceedings of IEEE FUZZ, pp. 1–8 (2013)Google Scholar
- 11.Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
- 13.Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005). http://www.cs.waikato.ac.nz/ml/weka/
- 15.Wu, J., Pan, S., Cai, Z., Zhu, X., Zhang, C.: Dual instance and attribute weighting for naive bayes classification. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1675–1679, IEEE (2014)Google Scholar
- 19.Zhang, C., Xue, G.R., Yu, Y., Zha, H.: Web-scale classification with naive bayes. In: Proceedings of WWW, pp. 1083–1084 (2009)Google Scholar