Evolved fuzzy min-max neural network for new-labeled data classification

Pattern classification is a fundamental problem in many data-driven application domains. New-labeled data refers to the data with the labels that are new and different from source labels. How to learn the new-labeled data is a crucial research in the data classification. In this paper, an evolved fuzzy min-max neural network for new-labeled data classification (FMM-NLA) is proposed. In FMM-NLA, the network can be self-evolved. Unlike the traditional FMM methods, the trained network of FMM-NLA can be expanded when new-labeled data added. FMM-NLA is a continuing-learning method, which can realize the continuing training process without retraining all the data. In order to verify the superiority of the proposed method, benchmark data sets are used. The experimental results show that FMM-NLA is effective in handling new-labeled data. Moreover, the application result on the pipeline defect recognition in depth shows that FMM-NLA is effective in solving the new-labeled defect recognition problem.

Automated summary

The paper introduces a new method for data classification - FMM-NLA, which uses evolved fuzzy min-max neural network to learn and classify new-labeled data. Compared to traditional methods, FMM-NLA can achieve continuous learning and expand the trained network without retraining all the data. Experimental results demonstrate the effectiveness of FMM-NLA in handling new-labeled data and defect recognition.