Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification

Tobey H. Ko; Zhonglei Gu; Yang Liu

Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification

Tobey H. Ko, Zhonglei Gu, Yang Liu

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

A model designed for automatic prediction of diseases based on multimedia data collected in hospitals is introduced in this working notes paper. In order to perform the automatic diseases prediction efficiently, while using as few data as possible for training, we develop a two-stage learning strategy, which first performs the weighted discriminant embedding (WDE) to project the original data to a low-dimensional feature subspace and then utilizes the cost-sensitive nearest neighbor (CS-NN) method in the learned subspace for disease prediction. The proposed approach is evaluated on the MediaEval 2018 Medico Multimedia Task. Copyright held by the owner/author(s).

Original language	English
Title of host publication	Working Notes Proceedings of the MediaEval 2018 Workshop
Editors	Martha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones
Publisher	CEUR-WS
Number of pages	3
Publication status	Published - Oct 2018
Event	MediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France Duration: 29 Oct 2018 → 31 Oct 2018 https://ceur-ws.org/Vol-2283/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
Volume	2283
ISSN (Print)	1613-0073

Conference

Conference	MediaEval 2018: Multimedia Benchmark Workshop
Country/Territory	France
City	Sophia Antipolis
Period	29/10/18 → 31/10/18
Internet address	https://ceur-ws.org/Vol-2283/

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http://ceur-ws.org/Vol-2283/MediaEval_18_paper_55.pdf

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Ko, T. H., Gu, Z., & Liu, Y. (2018). Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification. In M. Larson, P. Arora, C.-H. Demarty, M. Riegler, B. Bischke, E. Dellandrea, M. Lux, A. Porter, & G. J. F. Jones (Eds.), Working Notes Proceedings of the MediaEval 2018 Workshop (CEUR Workshop Proceedings; Vol. 2283). CEUR-WS. http://ceur-ws.org/Vol-2283/MediaEval_18_paper_55.pdf

Ko, Tobey H. ; Gu, Zhonglei ; Liu, Yang. / Weighted discriminant embedding : Discriminant subspace learning for imbalanced medical data classification. Working Notes Proceedings of the MediaEval 2018 Workshop. editor / Martha Larson ; Piyush Arora ; Claire-Hélène Demarty ; Michael Riegler ; Benjamin Bischke ; Emmanuel Dellandrea ; Mathias Lux ; Alastair Porter ; Gareth J. F. Jones. CEUR-WS, 2018. (CEUR Workshop Proceedings).

@inproceedings{2ccc50b9ab6b443385c1d54b5d27f06f,

title = "Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification",

abstract = "A model designed for automatic prediction of diseases based on multimedia data collected in hospitals is introduced in this working notes paper. In order to perform the automatic diseases prediction efficiently, while using as few data as possible for training, we develop a two-stage learning strategy, which first performs the weighted discriminant embedding (WDE) to project the original data to a low-dimensional feature subspace and then utilizes the cost-sensitive nearest neighbor (CS-NN) method in the learned subspace for disease prediction. The proposed approach is evaluated on the MediaEval 2018 Medico Multimedia Task. Copyright held by the owner/author(s).",

author = "Ko, {Tobey H.} and Zhonglei Gu and Yang Liu",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.; MediaEval 2018: Multimedia Benchmark Workshop ; Conference date: 29-10-2018 Through 31-10-2018",

year = "2018",

month = oct,

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

editor = "Martha Larson and Piyush Arora and Claire-H{\'e}l{\`e}ne Demarty and Michael Riegler and Benjamin Bischke and Emmanuel Dellandrea and Mathias Lux and Alastair Porter and Jones, {Gareth J. F.}",

booktitle = "Working Notes Proceedings of the MediaEval 2018 Workshop",

url = "https://ceur-ws.org/Vol-2283/",

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Ko, TH, Gu, Z & Liu, Y 2018, Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification. in M Larson, P Arora, C-H Demarty, M Riegler, B Bischke, E Dellandrea, M Lux, A Porter & GJF Jones (eds), Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR Workshop Proceedings, vol. 2283, CEUR-WS, MediaEval 2018: Multimedia Benchmark Workshop, Sophia Antipolis, France, 29/10/18. <http://ceur-ws.org/Vol-2283/MediaEval_18_paper_55.pdf>

Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification. / Ko, Tobey H.; Gu, Zhonglei; Liu, Yang.
Working Notes Proceedings of the MediaEval 2018 Workshop. ed. / Martha Larson; Piyush Arora; Claire-Hélène Demarty; Michael Riegler; Benjamin Bischke; Emmanuel Dellandrea; Mathias Lux; Alastair Porter; Gareth J. F. Jones. CEUR-WS, 2018. (CEUR Workshop Proceedings; Vol. 2283).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.

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AB - A model designed for automatic prediction of diseases based on multimedia data collected in hospitals is introduced in this working notes paper. In order to perform the automatic diseases prediction efficiently, while using as few data as possible for training, we develop a two-stage learning strategy, which first performs the weighted discriminant embedding (WDE) to project the original data to a low-dimensional feature subspace and then utilizes the cost-sensitive nearest neighbor (CS-NN) method in the learned subspace for disease prediction. The proposed approach is evaluated on the MediaEval 2018 Medico Multimedia Task. Copyright held by the owner/author(s).

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A2 - Riegler, Michael

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ER -

Weighted discriminant embedding: Discriminant subspace learning for imbalanced medical data classification

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