DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS

Yuqing Gao; Wing W. Y. Ng; Ting Wang; Sam Kwong

doi:10.1109/ICCICC46617.2019.9146039

DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS

Yuqing Gao
, Wing W. Y. Ng
, Ting Wang
, Sam Kwong

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Microarray data analysis has emerged as a strong tool for cancer diagnosis. Nevertheless, researches on it are significantly challenging as the microarray datasets are imbalanced and high-dimensional with relatively small sample size. In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to map the features for both minority and majority classes into a better classification representation. The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual Autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked Denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification.

Original language	English
Title of host publication	Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing
Publisher	IEEE
Pages	366-371
ISBN (Electronic)	978-1-7281-1419-4
ISBN (Print)	978-1-7281-0496-6
DOIs	https://doi.org/10.1109/ICCICC46617.2019.9146039
Publication status	Published - 1 Jul 2019
Event	18th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019 - Milan, Italy Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC

Conference

Conference	18th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019
Place	Italy
City	Milan
Period	23/07/19 → 25/07/19

UN SDGs

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

SDG 3 Good Health and Well-being

Research Keywords

denoising autoencoder
feature learning
imbalanced classification

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10.1109/ICCICC46617.2019.9146039

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Gao, Y., Ng, W. W. Y., Wang, T., & Kwong, S. (2019). DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS. In Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing (pp. 366-371). Article 9146039 (Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC ). IEEE. https://doi.org/10.1109/ICCICC46617.2019.9146039

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title = "DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS",

abstract = "Microarray data analysis has emerged as a strong tool for cancer diagnosis. Nevertheless, researches on it are significantly challenging as the microarray datasets are imbalanced and high-dimensional with relatively small sample size. In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to map the features for both minority and majority classes into a better classification representation. The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual Autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked Denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification.",

keywords = "denoising autoencoder, feature learning, imbalanced classification, denoising autoencoder, feature learning, imbalanced classification, denoising autoencoder, feature learning, imbalanced classification",

author = "Yuqing Gao and Ng, \{Wing W. Y.\} and Ting Wang and Sam Kwong",

year = "2019",

month = jul,

day = "1",

doi = "10.1109/ICCICC46617.2019.9146039",

language = "English",

isbn = "978-1-7281-0496-6",

series = "Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC ",

publisher = "IEEE",

pages = "366--371",

booktitle = "Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing",

address = "United States",

note = "18th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

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Gao, Y, Ng, WWY, Wang, T & Kwong, S 2019, DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS. in Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing., 9146039, Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC , IEEE, pp. 366-371, 18th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019, Milan, Italy, 23/07/19. https://doi.org/10.1109/ICCICC46617.2019.9146039

DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS. / Gao, Yuqing; Ng, Wing W. Y.; Wang, Ting et al.
Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing. IEEE, 2019. p. 366-371 9146039 (Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC ).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS

AU - Gao, Yuqing

AU - Ng, Wing W. Y.

AU - Wang, Ting

AU - Kwong, Sam

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Microarray data analysis has emerged as a strong tool for cancer diagnosis. Nevertheless, researches on it are significantly challenging as the microarray datasets are imbalanced and high-dimensional with relatively small sample size. In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to map the features for both minority and majority classes into a better classification representation. The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual Autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked Denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification.

AB - Microarray data analysis has emerged as a strong tool for cancer diagnosis. Nevertheless, researches on it are significantly challenging as the microarray datasets are imbalanced and high-dimensional with relatively small sample size. In this paper, we utilized Dual Denoising Autoencoder Features (DDAF), which integrates two Denoising Auto-Encoders (DAE) with different activation function to map the features for both minority and majority classes into a better classification representation. The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual Autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked Denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification.

KW - denoising autoencoder

KW - feature learning

KW - imbalanced classification

KW - denoising autoencoder

KW - feature learning

KW - imbalanced classification

KW - denoising autoencoder

KW - feature learning

KW - imbalanced classification

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85094682782&origin=recordpage

U2 - 10.1109/ICCICC46617.2019.9146039

DO - 10.1109/ICCICC46617.2019.9146039

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-7281-0496-6

T3 - Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC

SP - 366

EP - 371

BT - Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing

PB - IEEE

T2 - 18th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Gao Y, Ng WWY, Wang T, Kwong S. DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS. In Proceedings of The 18th International Conference on Cognitive Informatics and Cognitive Computing. IEEE. 2019. p. 366-371. 9146039. (Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC ). doi: 10.1109/ICCICC46617.2019.9146039

DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGNOSIS

Abstract

Publication series

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UN SDGs

Research Keywords

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