A SYNERGISTIC FEATURE SELECTION FRAMEWORK INTEGRATING STATISTICALTESTS AND SEQUENTIAL SELECTION FOR IMPROVED PLANT DISEASE DIAGNOSIS

Achieving high predictive accuracy while utilizing a minimal yet highly relevant set of features remains a critical challenge in machine learning. To address this, numerous feature selection techniques—ranging from ANOVA and LASSO to supervised methods like Mutual Information—have been developed, each offering unique strengths and limitations. Building upon these foundations, we introduce innovative hybrid approaches that seamlessly integrate filter and wrapper methods for more effective feature selection. These approaches were rigorously tested across multiple models. In this research, the new approaches of the integration of Chi-square test (with new step: relationship level) with SFS algorithm are proposed for enhancement model performance in feature selection. By the help of the new approaches, the trained results were 89% (in SVM), 88% (in kNN), 88% (in RF), 91% (in FCNN). These outputs are better than other feature selection methods’ results.