Name
Abstract | ||
|---|---|---|
Breast cancer is the most common tumor that seriously threatens the life of women. However, with imprecise measure methods, the detection results are not reliable enough, and this will bring more pain and cost to patients. Therefore, accurate identification of breast cancer is a very important issue. To tackle this problem, a composite firefly algorithm (named CoFA) is proposed, in which each firefly is attracted compositely by the best and two randomly selected fireflies. First, the composite attraction method increases the probability that the current firefly generates better solution. In addition, the two fireflies are randomly selected, whatever they are better or worse than current firefly, the population diversity can be improved. The proposed CoFA has been tested on several breast cancer datasets derived from UCI. Experimental results verified that CoFA significantly improves the recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/cpe.6032 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
breast cancer recognition, composite attraction strategy, evolutionary algorithm, firefly algorithm | Journal | 33 |
Issue | ISSN | Citations |
5 | 1532-0626 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hu Peng | 1 | 46 | 13.63 |
| Wenhua Zhu | 2 | 0 | 0.34 |
| Changshou Deng | 3 | 39 | 10.80 |
| Kun Yu | 4 | 0 | 0.34 |
| Zhijian Wu | 5 | 313 | 21.20 |