Name
Abstract | ||
|---|---|---|
When handling imbalanced datasets, the existing least learning machine ignores both the interrelationship between instances and the prior knowledge about the classes and hence tends to provide unfavorable accuracies across the classes. With the Laplacian matrix based loss function, Laplacian least learning machine (L2MM) is developed to address the infeasibility of the existing least learning machine for imbalanced data classification in this study. In order to find out the optimal number of hidden nodes in L2MM, by means of the derived update equations without any re-calculation of inverse matrix, its two incremental versions (i.e., with and without regularization) are invented for dynamically updating the hidden nodes in one-by-one way, thereby saving much training time. L2MM and its incremental versions inherit fast learning and good generalization capability of least learning machine. Experimental results on 19 benchmarking imbalanced datasets indicate the effectiveness of the proposed incremental L2MM for imbalanced classification tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.asoc.2019.106028 | Applied Soft Computing |
Keywords | DocType | Volume |
Least learning machine,Imbalanced classification,Laplacian matrix,Inverse matrix,Incremental learning of hidden nodes | Journal | 88 |
ISSN | Citations | PageRank |
1568-4946 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Zhou | 1 | 1 | 0.35 |
| Z. B. Jiang | 2 | 242 | 36.08 |
| Shitong Wang | 3 | 1485 | 109.13 |