Title: Fast classification algorithm of uncertain big data stream based on distributed limit learning machine

Authors: Zhiling Yang

Addresses: Department of Information Engineering, Zhujiang College of South China Agricultural University, Guangzhou, 510900, China

Abstract: Aiming at the problems of low classification accuracy and long classification time in traditional uncertain big data stream classification algorithm, a fast classification algorithm for uncertain big data stream based on distributed limit learning machine is proposed. First, construct the uncertain big data model, and measure the adaptive mixed distance of the data flow. Then, according to the measurement results, use the DSUFIM mine mining algorithm to mine the uncertain big data flow. Then combine the K-nearest neighbour and label correlation to conduct online feature screening for the mined uncertain big data flow, and use the cosine similarity method to detect whether the feature has conceptual drift. Finally, according to the detection results, the distributed limit learning machine is used to quickly classify uncertain big data streams. The experimental results show that the proposed algorithm has higher classification accuracy and faster classification speed for uncertain large data streams.

Keywords: distributed limit learning machine; uncertain big data flow; quick classification; K nearest neighbour; cosine similarity.

DOI: 10.1504/IJRIS.2025.145055

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.1, pp.73 - 81

Received: 22 Feb 2023
Accepted: 05 May 2023
Published online: 18 Mar 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article