An Incremental Kernel Extreme Learning Machine for Multi-Label Learning with Emerging New Labels

Abstract

Multi-label learning with emerging new labels is a practical problem that occurs in data streams and has become an important new research issue in the area of machine learning. However, existing models for dealing with this problem require high learning computational times, and there still exists a lack of research. Based on these issues, this paper presents an incremental kernel extreme learning machine for multi-label learning with emerging new labels, consisting of two parts: a novelty detector; and a multi-label classifier. The detector with free-user-setting threshold parameters was developed to identify instances with new labels. A new incremental multi-label classifier and its improved version were developed to predict a label set for each instance, which can add output units incrementally and update themselves in unlabeled instances. Comprehensive evaluations of the proposed method were carried out on the problems of multi-label classification with emerging new labels compared to comparative algorithms, which revealed the promising performance of the proposed method.