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This work compares supervised machine learning methods using reliable datafrom constructive simulations to estimate the most effective moment forlaunching missiles during air combat. We employed resampling techniques toimprove the predictive model, analyzing accuracy, precision, recall, andf1-score. Indeed, we could identify the remarkable performance of the modelsbased on decision trees and the significant sensitivity of other algorithms toresampling techniques. The models with the best f1-score brought values of0.379 and 0.465 without and with the resampling technique, respectively, whichis an increase of 22.69%. Thus, if desirable, resampling techniques can improvethe model's recall and f1-score with a slight decline in accuracy andprecision. Therefore, through data obtained through constructive simulations,it is possible to develop decision support tools based on machine learningmodels, which may improve the flight quality in BVR air combat, increasing theeffectiveness of offensive missions to hit a particular target.

Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations