Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm

Yunfeng Wu; S. C. Ng

doi:10.1109/IJCNN.2007.4371411

Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm

Yunfeng Wu
, S. C. Ng

Hong Kong Metropolitan University

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

4 Citations (Scopus)

Abstract

The diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts.

Original language	English
Title of host publication	The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages	2846-2850
Number of pages	5
DOIs	https://doi.org/10.1109/IJCNN.2007.4371411
Publication status	Published - 2007
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: 12 Aug 2007 → 17 Aug 2007

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Conference

Conference	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/Territory	United States
City	Orlando, FL
Period	12/08/07 → 17/08/07

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/IJCNN.2007.4371411

Cite this

Wu, Y., & Ng, S. C. (2007). Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 2846-2850). Article 4371411 (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371411

@inproceedings{c7df5e2dd65d434b95d66b6c067898a3,

title = "Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm",

abstract = "The diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts.",

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

year = "2007",

doi = "10.1109/IJCNN.2007.4371411",

language = "English",

isbn = "142441380X",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "2846--2850",

booktitle = "The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings",

note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",

}

Wu, Y & Ng, SC 2007, Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm. in The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings., 4371411, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2846-2850, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 12/08/07. https://doi.org/10.1109/IJCNN.2007.4371411

Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm. / Wu, Yunfeng; Ng, S. C.
The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 2846-2850 4371411 (IEEE International Conference on Neural Networks - Conference Proceedings).

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

TY - GEN

T1 - Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm

AU - Wu, Yunfeng

AU - Ng, S. C.

PY - 2007

Y1 - 2007

N2 - The diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts.

AB - The diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts.

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DO - 10.1109/IJCNN.2007.4371411

M3 - Conference contribution

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SN - 142441380X

SN - 9781424413805

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

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BT - The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings

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Wu Y, Ng SC. Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 2846-2850. 4371411. (IEEE International Conference on Neural Networks - Conference Proceedings). doi: 10.1109/IJCNN.2007.4371411

Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm

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