Application of Binary Adaptive Fish Migration Optimization Algor
As many redundant and irrelevant features exist, there is a challenge in classifying biology data. These features mislead the classi cation algorithms and increase the error rate of classi cation. Feature Selection (FS) can identify redundant features and remove them from the raw data to solve this problem. However, it is plagued by high computational costs and local optimization. The heuristic algorithm is utilized to solve this problem in this article. Adaptive Fish Migration Optimization.
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