Robust TSK Fuzzy System Based on Semisupervised Learning for Label Noise Data

As an important branch in the field of soft computing, TSK fuzzy systems have been diversely applied to supervised learning in recent years. However, real-world data may contain label noise, which has a negative impact on supervised learning. Label noise samples change the distribution of samples in each class, mislead learning algorithms and make classification problems more complicated. There are various sources of label noise, such as wrong assignment of labels during the data collection, contamination during the data storage, and so on. Thus, it is usually costly and time-consuming to obtain data with no label noise. When dealing with label noise data, existing TSK fuzzy system algorithms still have room for improvement. This article proposes a robust TSK fuzzy system based on semisupervised learning for label noise data (RTSK-FS-SS). By introducing an intuitionistic fuzzy set method, the proposed algorithm can detect label noise samples. An improved learning vector quantization is further adopted to overcome the challenge that traditional unsupervised learning-based antecedent part generation processes unable to make full use of the label information of training samples. Finally, we discard the label of suspicious samples and a consequent parameter learning method based on semisupervised learning is proposed. The proposed algorithm is validated using extensive experiments.