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

Tobey H. Ko, Zhonglei Gu, Yang Liu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-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 languageEnglish
Title of host publicationWorking Notes Proceedings of the MediaEval 2018 Workshop
EditorsMartha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones
PublisherCEUR-WS
Number of pages3
Publication statusPublished - Oct 2018
EventMediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018
https://ceur-ws.org/Vol-2283/

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume2283
ISSN (Print)1613-0073

Conference

ConferenceMediaEval 2018: Multimedia Benchmark Workshop
Country/TerritoryFrance
CitySophia Antipolis
Period29/10/1831/10/18
Internet address

Scopus Subject Areas

  • Computer Science(all)

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