Name
Title | ||
|---|---|---|
Optimal Randomness For Stochastic Configuration Network (Scn) With Heavy-Tailed Distributions |
Abstract | ||
|---|---|---|
Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Levy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Levy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Levy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/e23010056 | ENTROPY |
Keywords | DocType | Volume |
SCN, optimal randomness, heavy-tailed distribution, Lé, vy, Weibull, Cauchy | Journal | 23 |
Issue | ISSN | Citations |
1 | 1099-4300 | 1 |
PageRank | References | Authors |
0.38 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoyu Niu | 1 | 2 | 1.10 |
| Jiamin Wei | 2 | 1 | 0.38 |
| Yangquan Chen | 3 | 2257 | 242.16 |