Multi-objective evolutionary based feature selection supported by distributed multi-label classification and deep learning on image/video data

Gizem Nur Karagoz*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

We live in an era in which a myriad of computer systems produce immense amounts of (raw) data every day. This big data must be processed efficiently to gain valuable and hidden knowledge. Complex processing pipelines need to be designed for filtering out irrelevant data, also for efficient data mining and machine learning methods must be used to discover useful correlations in the big data. The purpose of this PhD research is the implementation of multi-objective evolutionary-based dimensionality reduction on a high volume of image/video data with the support of distributed multi-label classification algorithms.

Original languageEnglish
Title of host publicationMiddleware 2021 Doctoral Symposium - Proceedings of the 22nd International Middleware Conference
Subtitle of host publicationDoctoral Symposium
PublisherAssociation for Computing Machinery (ACM)
Pages6-7
Number of pages2
ISBN (Electronic)978-1-4503-9155-9
DOIs
Publication statusPublished - 2021
Event22nd International Middleware Conference, Middleware 2021 - Virtual, Online, Canada
Duration: 6 Dec 202110 Dec 2021

Publication series

NameMiddleware 2021 Doctoral Symposium - Proceedings of the 22nd International Middleware Conference: Doctoral Symposium

Conference

Conference22nd International Middleware Conference, Middleware 2021
Country/TerritoryCanada
CityVirtual, Online
Period6/12/2110/12/21

Keywords

  • big data processing
  • dimensionality reduction
  • distributed machine learning
  • feature engineering
  • feature extraction

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