@inproceedings{2930350496564c75a38dc0db67223a79,
title = "Data Stream Classification by Using Stacked CARU Networks",
abstract = "RNN based networks have been widely used in various applications to obtain impressive performance, and CARU has more advantages in NLP tasks. However, the RNN exerts a great pressure on the CARU unit when a single layer is used. In this work, we propose to implement a multi-layer design, which can gradually extract the main features through multiple CARU units. The advantage of this is that it can consider part of speech and content. It allows each layer to perform its work clearly while alleviating long-term dependencies. Using seven popular data streams, the performance of multi-layer CARU is compared and evaluated with many state-of-the-art technologies. Experiments show that our design can improve the classification performance of various data sets. In addition, the design and implementation can be easily deployed in RNN based systems.",
keywords = "CARU, Classification, Multilayer, RNN",
author = "Chan, {Ka Hou} and Im, {Sio Kei}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 ; Conference date: 17-01-2022 Through 20-01-2022",
year = "2022",
doi = "10.1109/BigComp54360.2022.00087",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "385--390",
editor = "Herwig Unger and Young-Kuk Kim and Eenjun Hwang and Sung-Bae Cho and Stephan Pareigis and Kyamakya Kyandoghere and Young-Guk Ha and Jinho Kim and Atsuyuki Morishima and Christian Wagner and Hyuk-Yoon Kwon and Yang-Sae Moon and Carson Leung",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022",
address = "United States",
}