Studying the effects of feature selection approaches on machine learning techniques for Mushroom classification problem

Feature selection is a critical factor affecting the performance of optimization algorithms. Without proper feature selection, optimization algorithms may suffer from slow convergence, overfitting, increased computational requirements, and longer execution times. On the other hand, omitting important features can lead to loss of relevant information, decreased accuracy, bias, and increased vulnerability to noise and outliers. This study investigates the use of genetic algorithms as a feature selection technique for a classification problem, specifically the mushrooms classification problem. Random forest is employed as the machine learning classifier, and genetic algorithms are compared with correlation as the feature selection method. The results show that genetic algorithms achieve higher accuracy, precision, recall, and F1-score compared to correlation-based feature selection. However, genetic algorithms have limitations in their applicability to specific optimization problems, the need for proper parameter setup, and longer convergence times. Despite these drawbacks, genetic algorithms prove to be superior to other feature selection techniques, particularly correlation-based approaches. This study highlights the importance of selecting appropriate feature selection techniques for optimization algorithms to improve their performance and achieve better results. In addition, this study explored the performance of various machine learning approaches on the complete mushroom dataset with 22 features and shows that genetic algorithms with feature selection as the most accurate method.