@inproceedings{c4556764edd5461a8f01197324c2ea58,
title = "Learning-based adaptation determination method for problem recognition of self-adaptive software",
abstract = "In this paper, we propose a method for identifying the adaptation period when a problem occurs in a system in order to reduce the unnecessary adaptation of self-adaptive software. Consequently, the dangerous situation information is defined, the behavior information at the time of problem occurrence is learned, and the adaptive performance is determined by comparing it with the existing similar situations by using the k-nearest neighbors algorithm. By the use of the proposed method, a situation where an unnecessary adaptation process is performed while running the self-adaptive system could be avoided, system load may be reduced, and service quality may be enhanced.",
keywords = "Machine learning, Problem recognition, Self-adaptive software",
author = "Kwangsoo Seol and Baik, {Doo Kwon}",
note = "Funding Information: This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2012M3C4A7033346). Doo-Kwon Baik is corresponding author Publisher Copyright: {\textcopyright} 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.; 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 ; Conference date: 27-07-2015 Through 30-07-2015",
year = "2019",
language = "English",
series = "Proceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015",
publisher = "CSREA Press",
pages = "399--400",
editor = "{de la Fuente}, David and Roger Dziegiel and Kozerenko, {Elena B.} and LaMonica, {Peter M.} and Liuzzi, {Raymond A.} and Olivas, {Jose A.} and Todd Waskiewicz and George Jandieri and Arabnia, {Hamid R.}",
booktitle = "Proceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015",
}