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SEIR Immune Strategy for Instance Weighted Naive Bayes Classification

  • Shan XueEmail author
  • Jie Lu
  • Guangquan Zhang
  • Li Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

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.

Keywords

Naive bayes Classification Immune strategy SEIR 

Notes

Acknowledgments

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Lab of Decision Systems & e-Service Intelligence (DeSI), Centre for Quantum Computation & Intelligent Systems (QCIS), Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia
  2. 2.School of ManagementShanghai UniversityShanghaiChina

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