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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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2014-01, Vol.2014 (2014), p.1-11
Main Authors: Kolesov, Anton, Kamyshenkov, Dmitry, Litovchenko, Maria, Smekalova, Elena, Golovizin, Alexey, Zhavoronkov, Alex
Format: Article
Language:English
Subjects:
Aging
Algorithms
Area Under Curve
Artificial Intelligence
Cluster Analysis
Computational Biology - methods
Databases, Factual
Europe
Fungal Proteins - metabolism
Genes, Fungal
Humans
Pattern Recognition, Automated - methods
Reproducibility of Results
Software
Support Vector Machine
United States
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Summary: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.
ISSN:1748-670X
1748-6718
DOI:10.1155/2014/781807