title: On multilabel classification methods of incompletely labeled biomedical text data.
abstract: 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.
authors.affiliation: Center for Pediatric Hematology, Oncology, and Immunology, Moscow 117997, Russia ; Moscow Institute of Physics and Technology, Moscow 117303, Russia.
authors.forename: Anton
authors.identifier: 0000-0003-3553-8030
authors.initials: A
authors.lastname: Kolesov
authors.affiliation: Moscow Institute of Physics and Technology, Moscow 117303, Russia.
authors.forename: Dmitry
authors.initials: D
authors.lastname: Kamyshenkov
authors.affiliation: Center for Pediatric Hematology, Oncology, and Immunology, Moscow 117997, Russia ; Moscow Institute of Physics and Technology, Moscow 117303, Russia ; The Biogerontology Research Foundation, Reading W1J 5NE, UK.
authors.forename: Maria
authors.identifier: 0000-0003-1868-4933
authors.initials: M
authors.lastname: Litovchenko
authors.affiliation: Chemistry Department, Moscow State University, Moscow 119991, Russia.
authors.forename: Elena
authors.initials: E
authors.lastname: Smekalova
authors.affiliation: Moscow Institute of Physics and Technology, Moscow 117303, Russia.
authors.forename: Alexey
authors.initials: A
authors.lastname: Golovizin
authors.affiliation: Center for Pediatric Hematology, Oncology, and Immunology, Moscow 117997, Russia ; Moscow Institute of Physics and Technology, Moscow 117303, Russia ; The Biogerontology Research Foundation, Reading W1J 5NE, UK.
authors.forename: Alex
authors.initials: A
authors.lastname: Zhavoronkov
mesh_terms: D000375:Aging; D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D005060:Europe; D005656:Fungal Proteins / Q000378:metabolism; D005800:Genes, Fungal; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D060388:Support Vector Machine; D014481:United States
chemical_list: D005656:Fungal Proteins