A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

The vast majority of today’s data is collected and stored in enormous databases with a wide range characteristics that have little to do the overarching goal concept. Feature selection process choosing best features for classification problem, which improves classification’s accuracy. considered multi-objective optimization problem two objectives: boosting accuracy while decreasing feature count. To efficiently handle process, we propose this paper novel algorithm inspired by behavior waterwheel plants when hunting their prey how they update locations throughout exploration exploitation processes. proposed referred as binary plant (bWWPA). In particular approach, search space well technique’s mapping from continuous discrete spaces are both represented new model. Specifically, fitness cost functions factored into algorithm’s evaluation modeled mathematically. assess performance algorithm, set extensive experiments were conducted evaluated terms 30 benchmark datasets include low, medium, high dimensional features. comparison other recent algorithms, experimental findings demonstrate bWWPAperforms better than competing algorithms. addition, statistical analysis performed one-way analysis-of-variance (ANOVA) Wilcoxon signed-rank tests examine differences between compared These experiments’ results confirmed superiority effectiveness handling process.