Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling

Peisen Yuan, Shougang Ren, Huanliang Xu, Jin Chen

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

4 Scopus citations

Abstract

Phenotype-based chrysanthemum petal classification, integrated with genomic sequencing, is critical for studying chrysanthemum phenotypic taxonomy. In this article, a new pipeline has been established towards automatic classification of chrysanthemum flower petal types. First, a set of phenotypic data of chrysanthemum flowers was collected. Second, we adopted random forest to classify the chrysanthemum flower petal types, and adopted over-sampling techniques to address the imbalanced label problem. Third, we systematically evaluated feature combinations regarding their influences to the classification results. Experimental results show that our method can successfully classify chrysanthemum flower petal types on an imbalanced chrysanthemum data.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Pages275-278
Number of pages4
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/TerritorySpain
CityMadrid
Period12/3/1812/6/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Chrysanthemum
  • imbalanced data
  • oversampling
  • phenotypic classification
  • random forest

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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