مشخصات کلی Automatic classification of respiratory patterns involving missing data imputation techniques
نویسنده کتاب (Author):
Hernández Pereira, Elena; Álvarez Estévez, Diego; Moret Bonillo, Vicente
انتشارات (Publisher):
ویرایش و نوع فایل (Edition/Format):
Downloadable article : English
منبع (Database):
عنوان ژورنال (Publication):
elena-m-hernandez-pereira-diego-alvarez-estevez-vicente-moret-bonillo-automatic-classification-of-respiratory-patterns-involving-missing-data-imputation-techniques-biosystems-en
موضوع (Subject):
Respiratory pattern classification Missing data imputation Machine learning
توضیحات خلاصه (Summary):
[Abstract] A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that in general, the Self-Organising Map imputation method allows non-tree based classifiers to achieve improvements over the rest of the imputation methods in terms of the classification accuracy, and that the Feedforward neural network and the Random Forest classifiers offer the best performance regardless of the imputation method used. The improvements in terms of accuracy over the previous work of the authors are limited but the Feed Forward neural network model achieves promising results. Read more…
ژانر / فرم:info:eu-repo/semantics/article
موضوع:Internet resource
نوع منبع:Internet Resource, Article
تمام نویسندگان / همکاران: Hernández Pereira, Elena; Álvarez Estévez, Diego; Moret Bonillo, Vicente
شناسه OCLC:979265412
Language Note:English
فهرست محتوا:1537-5110 1537-5129 http://hdl.handle.net/2183/18101 10.1016/j.biosystemseng.2015.06.011
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