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On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data

Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely lab...

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Autores principales: Kolesov, Anton, Kamyshenkov, Dmitry, Litovchenko, Maria, Smekalova, Elena, Golovizin, Alexey, Zhavoronkov, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3920912/
https://www.ncbi.nlm.nih.gov/pubmed/24587817
http://dx.doi.org/10.1155/2014/781807
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author Kolesov, Anton
Kamyshenkov, Dmitry
Litovchenko, Maria
Smekalova, Elena
Golovizin, Alexey
Zhavoronkov, Alex
author_facet Kolesov, Anton
Kamyshenkov, Dmitry
Litovchenko, Maria
Smekalova, Elena
Golovizin, Alexey
Zhavoronkov, Alex
author_sort Kolesov, Anton
collection PubMed
description Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of these approaches are based on similarity measurements between data vectors. We performed the experiments on AgingPortfolio (text dataset) and then rechecked on the Yeast (nontext genetic data). We tried SVM and RF classifiers for the original datasets and then for the modified ones. For each dataset, our experiments demonstrated that both classification algorithms performed considerably better when preceded by the training set modification step.
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spelling pubmed-39209122014-03-02 On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data Kolesov, Anton Kamyshenkov, Dmitry Litovchenko, Maria Smekalova, Elena Golovizin, Alexey Zhavoronkov, Alex Comput Math Methods Med Research Article Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of these approaches are based on similarity measurements between data vectors. We performed the experiments on AgingPortfolio (text dataset) and then rechecked on the Yeast (nontext genetic data). We tried SVM and RF classifiers for the original datasets and then for the modified ones. For each dataset, our experiments demonstrated that both classification algorithms performed considerably better when preceded by the training set modification step. Hindawi Publishing Corporation 2014 2014-01-23 /pmc/articles/PMC3920912/ /pubmed/24587817 http://dx.doi.org/10.1155/2014/781807 Text en Copyright © 2014 Anton Kolesov et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kolesov, Anton
Kamyshenkov, Dmitry
Litovchenko, Maria
Smekalova, Elena
Golovizin, Alexey
Zhavoronkov, Alex
On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data
title On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data
title_full On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data
title_fullStr On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data
title_full_unstemmed On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data
title_short On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data
title_sort on multilabel classification methods of incompletely labeled biomedical text data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3920912/
https://www.ncbi.nlm.nih.gov/pubmed/24587817
http://dx.doi.org/10.1155/2014/781807
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