@inproceedings{727573bec9c240f693a3a7773502c1d4,
title = "Regression based on neural incremental attribute learning with correlation-based feature ordering",
abstract = "Incremental Attribute Learning (IAL) gradually trains features in one or more size, which can be used to solve regression problems. Previous studies showed that feature ordering is crucial to IAL, and features should be sorted by some criteria. This study proposed two new feature ordering methods based on feature's group correlation and individual correlation for different situations. Experimental results show that grouped correlation-based feature ordering approach can exhibit better performance than others based on IAL neural networks in regression. Moreover, the performance of this approach is more stable than individual correlation-based approaches and some other approaches.",
keywords = "feature correlation, feature ordering, incremental attribute learning, neural networks, regression",
author = "Ting Wang and Xiaoyan Zhu and Guan, {Sheng Uei} and Man, {Ka Lok} and Ting, {T. O.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and the 7th IEEE International Conference on Robotics, Automation and Mechatronics, RAM 2015 ; Conference date: 15-07-2015 Through 17-07-2015",
year = "2015",
month = sep,
day = "23",
doi = "10.1109/ICCIS.2015.7274557",
language = "English",
series = "Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "109--113",
booktitle = "Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015",
}