Evolved Fuzzy Min-Max Neural Network for Unknown Labeled Data and its Application on Defect Recognition in Depth

Pattern classification is one of the most important issue in the data-driven application domains. Unlike the traditional unlabeled data, unknown labeled data refers to the testing data that cannot be classified into the existed category in this paper. How to learn the unknown labeled data is a crucial issue in the data classification. In this paper, an evolved fuzzy min-max neural network for unknown labeled data classification (FMM-ULD) is proposed. In FMM-ULD, the unknown labeled data handling process is designed. Moreover, in the unknown labeled data handling process, a decision function and a threshold function are designed. In addition, FMM-ULD can realize further correction for the unsatisfactory data classification of the known category. The experimental results using UCI benchmark data set show that FMM-ULD get good performance for handling the unknown labeled data as a general method. In addition, the application result on the pipeline defect recognition in depth shows that FMM-ULD is effective in handling the real-application unknown labeled data problem.

Automated summary

This paper proposes an evolved fuzzy min-max neural network for unknown labeled data classification, which can effectively handle and correct the classification of unknown labeled data. Experimental results demonstrate that the model performs well in handling unknown labeled data and is suitable for practical applications.