Offline Substitution Machine Learning Model for the Prediction of Fitness of GA-ARM

Association rule mining (ARM) is one of the most popular tasks in the field of data mining, very useful for decision-making. It is an NP-hard problem for which Genetic algorithms have been widely used. This is due to the obtained competitive results. However, their main drawback is the fitness computation which is time-consuming, especially when working with huge data. To overcome this problem, we propose an offline approach in which we substitute the GA's fitness computation with a Machine Learning model. The latter will predict the quality of the different generated solutions during the search process. The performed tests on several well-known datasets of different sizes show the effectiveness of our approach.