Abstract
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.
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Hamdad, L., Laoufi, C., Amirat, R., Benatchba, K., Sadeg, S. (2023). Offline Substitution Machine Learning Model for the Prediction of Fitness of GA-ARM. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_11
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DOI: https://doi.org/10.1007/978-3-031-43078-7_11
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