To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. 
K-nearest neighbor method
To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. 
Genetic Algorithms
To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. 
Particle Swarm Optimization
Feature Selection for Optimized High-Dimensional Biomedical Data Using an Improved Shuffled Frog Leaping Algorithm.High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis.  In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy, and an adaptive transfer factor.  To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. 
Shuffled Frog Leaping Algorithm