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

Peisen Yuan; Shougang Ren; Huanliang Xu; Jin Chen

doi:10.1109/BIBM.2018.8621234

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

Peisen Yuan, Shougang Ren, Huanliang Xu, Jin Chen

Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Pages	275-278
Number of pages	4
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621234
State	Published - Jan 21 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Chrysanthemum
imbalanced data
oversampling
phenotypic classification
random forest

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2018.8621234

Cite this

Yuan, P., Ren, S., Xu, H., & Chen, J. (2019). Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 275-278). Article 8621234 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621234

Yuan, Peisen ; Ren, Shougang ; Xu, Huanliang et al. / Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. 2019. pp. 275-278 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling",

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.",

keywords = "Chrysanthemum, imbalanced data, oversampling, phenotypic classification, random forest",

author = "Peisen Yuan and Shougang Ren and Huanliang Xu and Jin Chen",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

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doi = "10.1109/BIBM.2018.8621234",

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Yuan, P, Ren, S, Xu, H & Chen, J 2019, Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621234, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, pp. 275-278, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621234

Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling. / Yuan, Peisen; Ren, Shougang; Xu, Huanliang et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. 2019. p. 275-278 8621234 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2019/1/21

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N2 - 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.

AB - 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.

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BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

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Yuan P, Ren S, Xu H, Chen J. Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. 2019. p. 275-278. 8621234. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621234

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

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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