Multi-criteria decision making based architecture selection for single-hidden layer feedforward neural networks

Ran WANG, Haoran XIE, Jiqiang FENG, Fu Lee WANG, Chen XU

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Architecture selection is a fundamental problem in artificial neural networks, which could be treated as a decision making process that evaluates, ranks, and makes choices from a set of network structures. Traditional methods evaluate a network structure by designing a criterion based on a validation model or an error bound model. On one hand, the time complexity of a validation model is usually high; on the other hand, different validation models or error bound models may lead to different (even conflicting) results, which post challenges to the traditional single criterion-based architecture selection methods. In the area of decision making, many problems employed multiple criteria since the performance is better than using a single criterion. In this paper, we propose a multi-criteria decision making based architecture selection algorithm for single-hidden layer feedforward neural networks trained by extreme learning machine. Two criteria are incorporated into the selection process, i.e., training accuracy and the Q-value estimated by the localized generalization error model. The training accuracy reflects the capability of the model on correctly categorizing the known samples, and the Q-value estimated by localized generalization error model reflects the size of the neighbourhood of training samples in which the model can predict unseen samples with confidence. By achieving a trade-off between these two criteria, a new architecture selection algorithm is proposed. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2017 Springer-Verlag GmbH Germany, part of Springer Nature.
Original languageEnglish
Pages (from-to)655-666
JournalInternational Journal of Machine Learning and Cybernetics
Volume10
Issue number4
Early online date18 Nov 2017
DOIs
Publication statusPublished - Apr 2019

Fingerprint

Feedforward neural networks
Decision making
Learning systems
Neural networks

Citation

Wang, R., Xie, H., Feng, J., Wang, F. L., & Xu, C. (2019). Multi-criteria decision making based architecture selection for single-hidden layer feedforward neural networks. International Journal of Machine Learning and Cybernetics, 10(4), 655-666. doi: 10.1007/s13042-017-0746-9

Keywords

  • Architecture selection
  • Extreme learning machine
  • Localized generalization error model
  • Multi-criteria decision making