Active learning with extreme learning machine for online imbalanced multiclass classification

Active learning (AL) can significantly reduce the cost of labeling instances. Extreme learning machine (ELM) has low computational cost, extremely fast training speed and strong generalization ability. Previous studies have shown that the combination of them can generate efficient learning models. Nevertheless, these researches did not focus on multiclass imbalanced data. Cold start may occur and the performance of classifier is also reduced due to the imbalanced distribution of categories. Moreover, there is no framework for processing stream-based data. To address these problems, an improved framework called AL for class incremental and weighted sequential ELM (AI-WSELM) is proposed in this paper, and its advantages are as follows: (1) similarity query and margin sampling were used to alleviate cold start and select uncertain instances, respectively, (2) an improved weighting strategy was used to tackle stream-based multiclass imbalanced distribution, (3) a class incremental mechanism was added to deal with new categories appeared in the subsequent batches, and (4) AI-WSELM greatly reduced the cost of labeling samples when ensuring classification performance. The simulation results show that the proposed model has satisfactory performance compared to the existing ELMs and the other related algorithms, which indicates the feasibility and effectiveness of AI-WSELM. (C) 2021 Elsevier B.V. All rights reserved.