Name
Title | ||
|---|---|---|
Multi-criteria decision making based architecture selection for single-hidden layer feedforward neural networks |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s13042-017-0746-9 | International Journal of Machine Learning and Cybernetics |
Keywords | Field | DocType |
Architecture selection, Extreme learning machine, Localized generalization error model, Multi-criteria decision making | Data mining,Architecture,Feedforward neural network,Multiple criteria,Computer science,Extreme learning machine,Selection algorithm,Artificial intelligence,Artificial neural network,Time complexity,Machine learning,Decision-making | Journal |
Volume | Issue | ISSN |
10 | 4 | 1868-808X |
Citations | PageRank | References |
1 | 0.35 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Wang | 1 | 439 | 24.42 |
| Haoran Xie | 2 | 450 | 71.21 |
| Ji-Qiang Feng | 3 | 42 | 6.70 |
| Fu Lee Wang | 4 | 926 | 118.55 |
| Chen Xu | 5 | 112 | 15.26 |