Written and published by Biogerontology Research Foundation staff in collaboration with Moscow Institute of Physics and Technology (Center for Pediatric Hematology, Oncology, and Immunology) and Moscow State University (Chemistry Department).
Kolesov A(1), Kamyshenkov D(2), Litovchenko M(3), Smekalova E(4), Golovizin A(2), Zhavoronkov A(3).
(1) Center for Pediatric Hematology, Oncology, and Immunology, Moscow 117997, Russia ; Moscow Institute of Physics and Technology, Moscow 117303, Russia.
(2) Moscow Institute of Physics and Technology, Moscow 117303, Russia.
(3) 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.
(4) Chemistry Department, Moscow State University, Moscow 119991, Russia.
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.