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. 2018 Aug 29;8(1):13009.
doi: 10.1038/s41598-018-31395-5.

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

Hua Chai  1 , Yong Liang  2 , Sai Wang  1 , Hai-Wei Shen  1
Affiliations

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

Hua Chai et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The work flow of proposed logistic regression model combining SSL and AL.
Algorithm 1
Algorithm 1
The algorithm of the semi-supervised logistic regression model.
Figure 2
Figure 2
The classification accuracy of different methods in simulation experiments.
Figure 3
Figure 3
The ROC curves of different methods in simulation experiments.
Figure 4
Figure 4
ROC curves obtained by different methods in real datasets (a) DLBCL (b) Prostate (c) GSE21050 (d) GSE32603.
Figure 5
Figure 5
The number of genes selected by different methods in real datasets.
See this image and copyright information in PMC

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