Bio-invasion: A prediction model based on multi-objective optimization

Ruijie Zhu, Caini Fan, Zidie Chen, Rugui Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In some real application scenarios, it is difficult to obtain enough labeled data and get an ideal classification result. Unsupervised learning and few-shot classification are widely used for this kind of tasks. In contrast to prior approaches, we propose a multi-objective optimization method, combining both location and image information. We establish models for these two kinds of information and calculate Pareto frontier in multi-objective programming, which enhances the interpretability with clear evaluation indicators. Our method is applied in the detection on Asian giant hornet and attains favorable results across few-shot datasets.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665429184
DOIs
StatePublished - 17 Aug 2021
Event2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021 - Xi�an, China
Duration: 17 Aug 202119 Aug 2021

Publication series

NameProceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021

Conference

Conference2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
Country/TerritoryChina
CityXi�an
Period17/08/2119/08/21

Keywords

  • Gaussian Mixture Model
  • Pareto frontier
  • convolutional neural network
  • few-shot dataset
  • prediction

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