TY - GEN
T1 - An improved regularized extreme learning machine based on symbiotic organisms search
AU - Zhang, Boyang
AU - Sun, Lingjie
AU - Yuan, Haiwen
AU - Lv, Jianxun
AU - Ma, Zhao
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/19
Y1 - 2016/10/19
N2 - In this paper, a novel data classification approach is proposed based on integration of regularized extreme learning machine and Symbiotic Organisms Search (SOS). In order to simplified the description, the new method is named as Sos-RELM, which mainly contains two phases. As is known, in compared with traditional classification paths, such as SVM, LS-SVM and BP, extreme learning machine expresses its excellent ability in term of accuracy and computing time. Hence, in the first phase, we utilize regularised extreme learning machine with the goal that the output weights can be rapidly calculated. Symbiotic Organisms Search is one of new metaheuristic algorithms with various operations to update the individuals, which outperform DE, GA, and PSO. According to this effective and efficient optimization approach, in the second phase, the set of input wights, hidden biases and regularization parameter are optimized using Symbiotic Organisms Search. And the experimental results indicates that Sos-RELM attain a good comprehensive performance.
AB - In this paper, a novel data classification approach is proposed based on integration of regularized extreme learning machine and Symbiotic Organisms Search (SOS). In order to simplified the description, the new method is named as Sos-RELM, which mainly contains two phases. As is known, in compared with traditional classification paths, such as SVM, LS-SVM and BP, extreme learning machine expresses its excellent ability in term of accuracy and computing time. Hence, in the first phase, we utilize regularised extreme learning machine with the goal that the output weights can be rapidly calculated. Symbiotic Organisms Search is one of new metaheuristic algorithms with various operations to update the individuals, which outperform DE, GA, and PSO. According to this effective and efficient optimization approach, in the second phase, the set of input wights, hidden biases and regularization parameter are optimized using Symbiotic Organisms Search. And the experimental results indicates that Sos-RELM attain a good comprehensive performance.
KW - Classification
KW - Regularized extreme learning machine
KW - Symbiotic Organisms Search
UR - https://www.scopus.com/pages/publications/84997456770
U2 - 10.1109/ICIEA.2016.7603849
DO - 10.1109/ICIEA.2016.7603849
M3 - 会议稿件
AN - SCOPUS:84997456770
T3 - Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016
SP - 1645
EP - 1648
BT - Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016
Y2 - 5 June 2016 through 7 June 2016
ER -