Artificial immune system for attribute weighted Naive Bayes classification

Jia Wu; Zhihua Cai; Sanyou Zeng; Xingquan Zhu

doi:10.1109/IJCNN.2013.6706818

Artificial immune system for attribute weighted Naive Bayes classification

Jia Wu, Zhihua Cai, Sanyou Zeng, Xingquan Zhu

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

38 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	The 2013 International Joint Conference on Neural Networks (IJCNN)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781467361286
DOIs	https://doi.org/10.1109/IJCNN.2013.6706818
Publication status	Published - 1 Aug 2013
Externally published	Yes
Event	2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, United States Duration: 4 Aug 2013 → 9 Aug 2013

Conference

Conference	2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/Territory	United States
City	Dallas
Period	4/08/13 → 9/08/13

Keywords

artificial immune systems
Bayes methods
pattern classification
probability
attribute weighted Naive Bayes classification
artificial immune system based weighted Naive Bayes classifier
AISWNB
immunity theory
optimal weight values
conditional independence assumption
conditional probability
artificial immune system search mechanism
learning process
Niobium
Immune system
Accuracy
Cloning
Sociology
Statistics
Mutual information

Access to Document

10.1109/IJCNN.2013.6706818

Cite this

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title = "Artificial immune system for attribute weighted Naive Bayes classification",

abstract = "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.",

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author = "Jia Wu and Zhihua Cai and Sanyou Zeng and Xingquan Zhu",

year = "2013",

month = aug,

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doi = "10.1109/IJCNN.2013.6706818",

language = "English",

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booktitle = "The 2013 International Joint Conference on Neural Networks (IJCNN)",

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Wu, J, Cai, Z, Zeng, S & Zhu, X 2013, Artificial immune system for attribute weighted Naive Bayes classification. in The 2013 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers (IEEE), pp. 1-8, 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, Texas, United States, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6706818

Artificial immune system for attribute weighted Naive Bayes classification. / Wu, Jia; Cai, Zhihua; Zeng, Sanyou et al.

The 2013 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers (IEEE), 2013. p. 1-8.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Artificial immune system for attribute weighted Naive Bayes classification

AU - Wu, Jia

AU - Cai, Zhihua

AU - Zeng, Sanyou

AU - Zhu, Xingquan

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N2 - 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.

AB - 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.

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KW - Bayes methods

KW - pattern classification

KW - probability

KW - attribute weighted Naive Bayes classification

KW - artificial immune system based weighted Naive Bayes classifier

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KW - immunity theory

KW - optimal weight values

KW - conditional independence assumption

KW - conditional probability

KW - artificial immune system search mechanism

KW - learning process

KW - Niobium

KW - Immune system

KW - Accuracy

KW - Cloning

KW - Sociology

KW - Statistics

KW - Mutual information

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DO - 10.1109/IJCNN.2013.6706818

M3 - Conference proceeding contribution

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EP - 8

BT - The 2013 International Joint Conference on Neural Networks (IJCNN)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013

Y2 - 4 August 2013 through 9 August 2013

ER -

Artificial immune system for attribute weighted Naive Bayes classification

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