2016/06/02 by Yue Deng, Zhiquan Ren, Youyong Kong +2 · 5 citations
Computer Science · #Anomaly Detection Techniques and Applications #Fuzzy Logic and Control Systems #Neural Networks and Applications
paper · doi:10.1109/tfuzz.2016.2574915
openalex publication_date 2016/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-driven feature learning from big data. However, typical DL is a fully deterministic model that sheds no light on data uncertainty reductions. In this paper, we show how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation. The bulk of the proposed fuzzy system is a hierarchical deep neural network that derives information from both fuzzy and neural representations. Then, the knowledge learnt from these two respective views are fused altogether forming the final data representation to be classified. The effectiveness of the model is verified on three practical tasks of image categorization, high-frequency financial data prediction and brain MRI segmentation that all contain high level of uncertainties in the raw data. The fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.