Online sequential extreme learning machine with the increased classes. (March 2021)

• Record Type: Journal Article
• Authors: Yu, Hualong; Xie, Houjuan; Yang, Xibei; Zou, Haitao; Gao, Shang
• Is Part Of: Computers & electrical engineering. Volume 90(2021)
• Publication Date: 2021-03
• Subjects: Online learning: extreme learning machine (ELM) -- Increased classes -- Online sequential extreme learning machine (OS-ELM) -- Hierarchical structure; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Online learning with the increased classes is considered. Two different types of the increased class are indicated. The structure of OS-ELM is changed to adapt two different increased class types. Results indicate that the proposed algorithm can adapt the increased class well. Abstract: In this paper, we investigate the online learning problem in a specific open environment, i.e., the number of classes can be dynamically increased. Online sequential extreme learning machine (OS-ELM) is extended to address the problem of the increased classes. Specifically, two different increased classes scenarios are considered. The first scenario is that the new classes, which haven't appeared in the previous instances, emerge in the new received data. The other scenario is that in data stream, an old class is split into several new subclasses due to some specific reasons. For the first kind of scenario, OS-ELM is inserted an alternative output node which can be extended whenever the new class instances are received. While for the second kind of scenario, we adopt a hierarchical structure to adapt the new split classes. We adopt the simple experiments to show the effectiveness and feasibility of the proposed models.