학술논문
StreamSoNG: A Soft Streaming Classification Approach
Document Type
Periodical
Author
Source
IEEE Transactions on Emerging Topics in Computational Intelligence IEEE Trans. Emerg. Top. Comput. Intell. Emerging Topics in Computational Intelligence, IEEE Transactions on. 6(3):700-709 Jun, 2022
Subject
Language
ISSN
2471-285X
Abstract
Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned a crisp label (into one of the structures) and that structure's footprint is incrementally updated. There is no reason that these assignments need to be crisp. In this paper, we propose a new streaming classification algorithm that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (of typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm. The approach is tested on synthetic and real image datasets. We compare our approach to three other streaming classifiers based on the Adaptive Random Forest, Very Fast Decision Rules, and the DenStream algorithm with excellent results.