A novel logistic regression model combining semi-supervised learning and active learning for disease classification.

Scientific Reports
Hua ChaiHai-Wei Shen

Abstract

Traditional supervised learning classifier needs a lot of labeled samples to achieve good performance, however in many biological datasets there is only a small size of labeled samples and the remaining samples are unlabeled. Labeling these unlabeled samples manually is difficult or expensive. Technologies such as active learning and semi-supervised learning have been proposed to utilize the unlabeled samples for improving the model performance. However in active learning the model suffers from being short-sighted or biased and some manual workload is still needed. The semi-supervised learning methods are easy to be affected by the noisy samples. In this paper we propose a novel logistic regression model based on complementarity of active learning and semi-supervised learning, for utilizing the unlabeled samples with least cost to improve the disease classification accuracy. In addition to that, an update pseudo-labeled samples mechanism is designed to reduce the false pseudo-labeled samples. The experiment results show that this new model can achieve better performances compared the widely used semi-supervised learning and active learning methods in disease classification and gene selection.

References

Jun 28, 2002·Cancer Cell·Dinesh SinghWilliam R Sellers
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May 30, 2014·Tumour Biology : the Journal of the International Society for Oncodevelopmental Biology and Medicine·Feng LiuGuoqiang Zhang
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yu-Feng Li, Zhi-Hua Zhou
Jan 17, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Liang LinLei Zhang
Jan 18, 2017·The Journal of Pathology·Panimaya Jeffreena MirandaYgal Haupt
May 2, 2017·Tumour Biology : the Journal of the International Society for Oncodevelopmental Biology and Medicine·Marwa TarekIman F Montasser
Jul 4, 2017·Tumour Biology : the Journal of the International Society for Oncodevelopmental Biology and Medicine·Yang ZhouZhi-Li Liu

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Citations

Aug 20, 2020·PloS One·Guilherme CamargoPriscila T M Saito
Jul 12, 2019·Chemical Reviews·Xin YangShengyong Yang

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Datasets Mentioned

BETA
GSE21050
54613
310
GSE32603

Software Mentioned

ASSL
Auto
SSL
AL
lo

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