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.

模式分类是数据驱动的应用程序领域中最重要的问题之一。与传统的未标记数据不同，未知标记数据是指无法归类为现有类别的测试数据。如何学习未知的标记数据是数据分类中的关键问题。本文提出了一种改进的模糊最小-最大神经网络，用于未知标记数据分类（FMM-ULD）。在FMM-ULD中，设计了未知标签数据处理过程。此外，在未知标记数据处理过程中，设计了决策函数和阈值函数。另外，FMM-ULD可以实现对已知类别的数据分类不满意的进一步校正。使用UCI基准数据集的实验结果表明，FMM-ULD作为通用方法可以很好地处理未知标签数据。此外，在管道缺陷识别方面的应用结果表明，FMM-ULD在处理实际应用中未知标签数据问题方面是有效的。