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- Title
自训练新类探测半监督学习算法.
- Authors
何玉林; 陈佳琪; 黄启航; Philippe Fournier-Viger; 黄哲学
- Abstract
The limited application scenario and unsatisfactory generalization capability are two main defects of traditional semi- supervised learning (SSL) algorithms. Especially, their prediction capabilities will be severely degraded when the training dataset includes the samples with new labels. It is usually time-consuming and expensive to label the unlabeled samples by the domain experts. In addition, the wrongly-labeled samples are unavoidable due to the insufficient background knowledge. Therefore, the SSL algorithms that can correctly label the unlabeled samples with unseen labels are urgent for practical applications. After analyzing the SSL algorithm in detail, an effective new class detection SSL (NCD-SSL) algorithm is proposed. Firstly, a universal incremental extreme learning machine is designed to deal with both class- incremental and sample- incremental classification problems. Secondly, the selftraining model is improved by using the samples with high-confidence labels and setting a buffer pool to store the samples with low- confidence labels. Thirdly, the samples in buffer pool are further handled with clustering and distribution consistency judgement technologies so that the new classes can be detected. Finally, a series of persuasive experiments are conducted to validate the rationality and effectiveness of NCD- SSL algorithm on synthetic datasets and real datasests. Experimental results show that the testing accuracies of NCD- SSL algorithm are increased more than 30, 20 and 10 percentage points for 3-classes, 2-classes, 1-class missing cases in comparison with the other six popular SSL algorithms and thus demonstrate superior SSL performances of NCD-SSL algorithm.
- Publication
Journal of Frontiers of Computer Science & Technology, 2023, Vol 17, Issue 9, p2184
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2206059