Exploiting ensemble diversity for automatic feature extraction

G. BROWN; Xin YAO; J. WYATT; H. WERSING; B. SENDHOFF

doi:10.1109/ICONIP.2002.1198981

Exploiting ensemble diversity for automatic feature extraction

G. BROWN
, Xin YAO
, J. WYATT
, H. WERSING
, B. SENDHOFF

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

10 Citations (Scopus)

Abstract

We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high-dimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context. © 2002 Nanyang Technological University.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1786-1790
Number of pages	5
Volume	4
ISBN (Print)	9810475241
DOIs	https://doi.org/10.1109/ICONIP.2002.1198981
Publication status	Published - 27 Aug 2003
Externally published	Yes

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10.1109/ICONIP.2002.1198981

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BROWN, G., YAO, X., WYATT, J., WERSING, H., & SENDHOFF, B. (2003). Exploiting ensemble diversity for automatic feature extraction. In Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. (Vol. 4, pp. 1786-1790). Article 1198981 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1198981

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abstract = "We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high-dimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context. {\textcopyright} 2002 Nanyang Technological University.",

author = "G. BROWN and Xin YAO and J. WYATT and H. WERSING and B. SENDHOFF",

year = "2003",

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doi = "10.1109/ICONIP.2002.1198981",

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Exploiting ensemble diversity for automatic feature extraction. / BROWN, G.; YAO, Xin; WYATT, J. et al.
Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.. Vol. 4 Institute of Electrical and Electronics Engineers Inc., 2003. p. 1786-1790 1198981.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Exploiting ensemble diversity for automatic feature extraction

AU - BROWN, G.

AU - YAO, Xin

AU - WYATT, J.

AU - WERSING, H.

AU - SENDHOFF, B.

PY - 2003/8/27

Y1 - 2003/8/27

N2 - We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high-dimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context. © 2002 Nanyang Technological University.

AB - We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high-dimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context. © 2002 Nanyang Technological University.

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U2 - 10.1109/ICONIP.2002.1198981

DO - 10.1109/ICONIP.2002.1198981

M3 - Conference paper (refereed)

SN - 9810475241

VL - 4

SP - 1786

EP - 1790

BT - Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Exploiting ensemble diversity for automatic feature extraction

Abstract

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