Continual learning classification method with human-in-the-loop

The classification problem is essential to machine learning, often used in fault detection, condition monitoring, and behavior recognition. In recent years, due to the rapid development of incremental learning, reinforcement learning, transfer learning, and continual learning algorithms, the contradiction between the classification model and new data has been alleviated. However, due to the lack of feedback, most classification algorithms take long to search and may deviate from the correct results. Because of this, we propose a continual learning classification method with human-in-the-loop (H—CLCM) based on the artificial immune system. H—CLCM draws lessons from the mechanism that humans can enhance immune response through various intervention technologies and brings humans into the test learning process in a supervisory role. The human experience is integrated into the test phase, and the parameters corresponding to the error identification data are adjusted online. It enables it to converge to an accurate prediction model at the lowest cost and to learn new data categories without retraining the classifier. • All necessary steps and formulas of H—CLCM are provided. • H—CLCM adds manual intervention to improve the classification ability of the model. • H—CLCM can recognize new types of data.