Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Samal, Sidharth | Dash, Rajashree*
Affiliations: Computer Science and Engineering Department, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
Correspondence: [*] Corresponding author: Rajashree Dash, Computer Science and Engineering Department, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India. E-mail: [email protected].
Abstract: In support of its faster learning capacity and better generalization, Extreme Learning Machine (ELM) has gained the attention of researchers as a means to solve various real-world prediction problems. However, the performance of ELM is heavily dependent on the activation functions used in it. In this study, design of ELM is addressed as a Multi Criteria Decision Making (MCDM) problem. The selection of the activation function for the ELM based predictor model is done through a novel MCDM ensemble approach. On the basis of 9 prediction metrics, MCDM techniques such as TOPSIS, PROMETHEE-II, and VIKOR were used to assess and rank 15 activation functions on ELM performance. In light of the fact that the ranks determined by each MCDM technique do not coincide, a novel ensemble approach was proposed to calculate the final rank score by considering the occurrences of each model in the primary ranking and its respective rank score. In the end, the most highly ranked activation function is taken into account in the ELM-based predictor model. The proposed model is assessed over three benchmark stock indices such as BSE SENSEX, NIFTY 50 and BSE S&P 500. The empirical analysis clearly shows that the ELM based predictor model designed using ELU activation function performs competitively compared to other reported models.
Keywords: Forecasting, ANN, ELM, MCDM
DOI: 10.3233/IDT-210152
Journal: Intelligent Decision Technologies, vol. 16, no. 2, pp. 387-406, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]