A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications

作者：Wu, Di; Luo, Xin; Wang, Guoyin^*; Shang, Mingsheng^*; Yuan, Ye; Yan, Huyong

来源：IEEE Transactions on Industrial Informatics, 2018, 14(3): 909-920.

DOI：10.1109/TII.2017.2737827

• Differential evolution (DE)
• general framework
• industrial application
• positioning optimization
• self-labeled
• semi-supervised classification (SSC)

Self-labeled technique, a paradigm of semisupervised classification (SSC), is highly effective in alleviating the shortage of labeled data in classification tasks via an iterative self-labeling process. Although existing self-labeled SSC models show great prospect in industrial applications, they suffer from performance degeneration caused by false-positive label-predictions of unlabeled data during the iterative self-labeling process. For addressing this issue, this paper proposes a novel SSC framework, which is highly compatible with most existing self-labeled SSC models. The main idea of this framework is to incorporate a differential-evolution-based positioning optimization algorithm for classification into the iterative self-labeling process, aiming at optimizing the positioning of newly labeled data. Specifically, five representative self-labeled SSC models with different characteristics are modified based on the proposed framework to check their performances. Experimental results on 45 benchmark datasets demonstrate that the proposed framework is highly compatible with tested self-labeled SSC models, and significantly effective in improving their performances.

• 出版日期2018-3
• 单位中国科学院重庆绿色智能技术研究院; 中国科学院大学; 重庆工商大学