A Novel Progressive Multi-label Classifier for Classincremental Data
This addresses the need for handling streaming data with unknown label counts in applications like robotics, representing an incremental advancement in multi-label classification.
The paper tackles the problem of multi-label classification in class-incremental learning by designing a progressive algorithm that adds new output neurons and restructures neural network connections to retain knowledge of previous labels. Experimental results on benchmark datasets validate its efficiency and effectiveness.
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added and the neural network connections and parameters are automatically restructured as if the label has been introduced from the beginning. This work is the first of the kind in multi-label classifier for class-incremental learning. It is useful for real-world applications such as robotics where streaming data are available and the number of labels is often unknown. Based on the Extreme Learning Machine framework, a novel universal classifier with plug and play capabilities for progressive multi-label classification is developed. Experimental results on various benchmark synthetic and real datasets validate the efficiency and effectiveness of our proposed algorithm.