Combining neural learners with the naive bayes fusion rule for breast tissue classification

Yunfeng Wu; S. C. Ng

doi:10.1109/ICIEA.2007.4318498

Combining neural learners with the naive bayes fusion rule for breast tissue classification

Yunfeng Wu, S. C. Ng

Hong Kong Metropolitan University

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

2 Citations (Scopus)

Abstract

Early detection of suspicious breast lesions is commonly performed by analysis of breast profiles detected by effective modalities. Tissue distribution in each modality can provide important information about the elastic characteristics of breast which is useful for computer-aided diagnosis. In this paper, the Naive Bayes (NB) fusion rule is utilized to combine a group of Radial Basis Function (RBF) neural learners in a multiple classifier system for classification of breast tissues. The empirical results show the NB fusion rule may effectively diminish the mean-squared errors, and also improve approximately 15% classification accuracy, which is significantly better than the component RBF neural learners. Moreover, the NB fusion rule also outperforms the widely used simple average and majority voting fusion rules.

Original language	English
Title of host publication	ICIEA 2007
Subtitle of host publication	2007 Second IEEE Conference on Industrial Electronics and Applications
Pages	709-713
Number of pages	5
DOIs	https://doi.org/10.1109/ICIEA.2007.4318498
Publication status	Published - 2007
Event	2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007 - Harbin, China Duration: 23 May 2007 → 25 May 2007

Publication series

Name	ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications

Conference

Conference	2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007
Country/Territory	China
City	Harbin
Period	23/05/07 → 25/05/07

Keywords

Breast cancer diagnosis
Classification
Ensemble
Multiple classier system
Naive bayes rule
Neural networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICIEA.2007.4318498

Cite this

@inproceedings{3603976287444226931959bab3e7f74c,

title = "Combining neural learners with the naive bayes fusion rule for breast tissue classification",

abstract = "Early detection of suspicious breast lesions is commonly performed by analysis of breast profiles detected by effective modalities. Tissue distribution in each modality can provide important information about the elastic characteristics of breast which is useful for computer-aided diagnosis. In this paper, the Naive Bayes (NB) fusion rule is utilized to combine a group of Radial Basis Function (RBF) neural learners in a multiple classifier system for classification of breast tissues. The empirical results show the NB fusion rule may effectively diminish the mean-squared errors, and also improve approximately 15% classification accuracy, which is significantly better than the component RBF neural learners. Moreover, the NB fusion rule also outperforms the widely used simple average and majority voting fusion rules.",

keywords = "Breast cancer diagnosis, Classification, Ensemble, Multiple classier system, Naive bayes rule, Neural networks",

author = "Yunfeng Wu and Ng, {S. C.}",

year = "2007",

doi = "10.1109/ICIEA.2007.4318498",

language = "English",

isbn = "1424407370",

series = "ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications",

pages = "709--713",

booktitle = "ICIEA 2007",

note = "2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007 ; Conference date: 23-05-2007 Through 25-05-2007",

}

Wu, Y & Ng, SC 2007, Combining neural learners with the naive bayes fusion rule for breast tissue classification. in ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications., 4318498, ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications, pp. 709-713, 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, Harbin, China, 23/05/07. https://doi.org/10.1109/ICIEA.2007.4318498

Combining neural learners with the naive bayes fusion rule for breast tissue classification. / Wu, Yunfeng; Ng, S. C.
ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications. 2007. p. 709-713 4318498 (ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications).

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

TY - GEN

T1 - Combining neural learners with the naive bayes fusion rule for breast tissue classification

AU - Wu, Yunfeng

AU - Ng, S. C.

PY - 2007

Y1 - 2007

N2 - Early detection of suspicious breast lesions is commonly performed by analysis of breast profiles detected by effective modalities. Tissue distribution in each modality can provide important information about the elastic characteristics of breast which is useful for computer-aided diagnosis. In this paper, the Naive Bayes (NB) fusion rule is utilized to combine a group of Radial Basis Function (RBF) neural learners in a multiple classifier system for classification of breast tissues. The empirical results show the NB fusion rule may effectively diminish the mean-squared errors, and also improve approximately 15% classification accuracy, which is significantly better than the component RBF neural learners. Moreover, the NB fusion rule also outperforms the widely used simple average and majority voting fusion rules.

AB - Early detection of suspicious breast lesions is commonly performed by analysis of breast profiles detected by effective modalities. Tissue distribution in each modality can provide important information about the elastic characteristics of breast which is useful for computer-aided diagnosis. In this paper, the Naive Bayes (NB) fusion rule is utilized to combine a group of Radial Basis Function (RBF) neural learners in a multiple classifier system for classification of breast tissues. The empirical results show the NB fusion rule may effectively diminish the mean-squared errors, and also improve approximately 15% classification accuracy, which is significantly better than the component RBF neural learners. Moreover, the NB fusion rule also outperforms the widely used simple average and majority voting fusion rules.

KW - Breast cancer diagnosis

KW - Classification

KW - Ensemble

KW - Multiple classier system

KW - Naive bayes rule

KW - Neural networks

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U2 - 10.1109/ICIEA.2007.4318498

DO - 10.1109/ICIEA.2007.4318498

M3 - Conference contribution

AN - SCOPUS:35248816507

SN - 1424407370

SN - 9781424407378

T3 - ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications

SP - 709

EP - 713

BT - ICIEA 2007

T2 - 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007

Y2 - 23 May 2007 through 25 May 2007

ER -

Combining neural learners with the naive bayes fusion rule for breast tissue classification

Abstract

Publication series

Conference

Keywords

UN SDGs

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