Fuzzy ARTMAP Network and Clustering for Streaming Classification under Emerging New Classes

Yiyang Xu, Yan Li, Yunjie Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Streaming classification with emerging new classes is a challenging problem receiving extensive attention. To date, most approaches simply detect novel classes without further recognizing them, and ignore conceptual drift and conceptual evolution problem. Therefore, in this paper, we propose an effective semi-supervised framework that combines ARTMAP neural network and clustering method to detect and identify simultaneously multiple known and unknown classes in data streams and update the network online. Experiments have been conducted on four benchmark datasets with different data forms including MNIST, CIFAR10, network attack analysis, and geospatial information of forests. The empirical evaluation shows effectiveness of the proposed approach, whose results are much better than many previous studies.

Original languageEnglish
Title of host publicationICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123455
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China
Duration: 11 Dec 201913 Dec 2019

Publication series

NameICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/TerritoryChina
CityChongqing
Period11/12/1913/12/19

Keywords

  • ARTMAP
  • SENC
  • clustering
  • model update

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