@inproceedings{717f66e28bb54de59909b9f69f8474be,
title = "Seir immune strategy for instance weighted naive Bayes classification",
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 = "Classification, Immune strategy, Naive bayes, SEIR",
author = "Shan Xue and Jie Lu and Guangquan Zhang and Li Xiong",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-26532-2_31",
language = "English",
isbn = "9783319265315",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-VDI-Verlag GmbH & Co. KG",
pages = "283--292",
editor = "Sabri Arik and Tingwen Huang and Lai, {Weng Kin} and Qingshan Liu",
booktitle = "Neural Information Processing",
address = "Germany",
note = "22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
}