Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4943070 | Expert Systems with Applications | 2017 | 54 Pages |
Abstract
In this study, ACBFO and ISEDBFO are tested with 10 public data sets of UCI. The performance of the proposed methods is compared with particle swarm optimization based, genetic algorithm based, simulated annealing based, ant lion optimization based, binary bat algorithm based and cuckoo search based approaches. The experimental results demonstrate that the average classification accuracy of the proposed algorithms is nearly 3 percentage points higher than other tested methods. Furthermore, the improved algorithms reduce the length of the feature subset by almost 3 in comparison to other methods. In addition, the modified methods achieve excellent performance on wilcoxon signed-rank test and sensitivity-specificity test. In conclusion, the novel BFO algorithms can provide important support for the expert and intelligent systems.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Chen Yu-Peng, Li Ying, Wang Gang, Zheng Yue-Feng, Xu Qian, Fan Jia-Hao, Cui Xue-Ting,