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Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation
Ye, Lin1,2; Chang, Chun-Yi1; Hsieh, Chih-hao1,3
2011
Source PublicationMARINE ECOLOGY PROGRESS SERIES
ISSN0171-8630
Volume441Pages:185-196
AbstractZooplankton play a critical role in aquatic ecosystems and are commonly used as bio-indicators to assess anthropogenic and climate impacts. Nevertheless, traditional microscope-based identification of zooplankton is inefficient. To overcome the low efficiency, computer-based methods have been developed. Yet, the performance of automated classification remains unsatisfactory because of the low accuracy of recognition. Here we propose a novel framework for automated plankton classification based on a naive Bayesian classifier (NBC). We take advantage of the posterior probability of NBC to facilitate category aggregation and to single out objects of low predictive confidence for manual re-classifying in order to achieve a high level of final accuracy. This method was applied to East China Sea zooplankton samples with 154 289 objects, and the Bayesian automated zooplankton classification model showed a reasonable overall accuracy of 0.69 in unbalanced and 0.68 in balanced training for 25 planktonic and 1 aggregated non-planktonic categories. More importantly, after manually checking 17 to 38% of the objects of low confidence (depending on how one defines 'low confidence'), the final accuracy increased to 0.85-0.95 in the unbalanced training case, and after checking 18 to 42% of the low-confidence objects in the balanced training case, the final accuracy increased to 0.84-0.95. Our semi-automated approach is significantly more accurate than automated classifiers in recognizing rare categories, thereby facilitating ecological applications by improving the estimates of taxa richness and diversity. Our approach can make up for the deficiencies in current automated zooplankton classifiers and facilitates an efficient semi-automated zooplankton classification, which may have a broad application in environmental monitoring and ecological research.
SubtypeArticle
KeywordAutomated Classification Naive Bayesian Classifier Predictive Confidence Rapid Category Aggregation Zooplankton Community Zooscan
DOI10.3354/meps09387
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Physical Sciences
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology ; Oceanography
WOS SubjectEcology ; Marine & Freshwater Biology ; Oceanography
WOS KeywordKERNEL DENSITY-ESTIMATION ; NEURAL-NETWORK ANALYSIS ; WESTERN NORTH PACIFIC ; FLOW-CYTOMETRIC DATA ; BANDWIDTH SELECTION ; IDENTIFICATION ; PLANKTON ; BIODIVERSITY ; PHYTOPLANKTON ; CLIMATE
WOS IDWOS:000298061000016
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/342005/28798
Collection淡水生态学研究中心
Affiliation1.Natl Taiwan Univ, Inst Oceanog, Taipei 10617, Taiwan
2.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
3.Natl Taiwan Univ, Inst Ecol & Evolutionary Biol, Taipei 10617, Taiwan
Recommended Citation
GB/T 7714
Ye, Lin,Chang, Chun-Yi,Hsieh, Chih-hao. Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation[J]. MARINE ECOLOGY PROGRESS SERIES,2011,441:185-196.
APA Ye, Lin,Chang, Chun-Yi,&Hsieh, Chih-hao.(2011).Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation.MARINE ECOLOGY PROGRESS SERIES,441,185-196.
MLA Ye, Lin,et al."Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation".MARINE ECOLOGY PROGRESS SERIES 441(2011):185-196.
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