Abstract
The Patient Rule Induction Method (PRIM) is a bump hunting algorithm that generates a big number of rules in high dimensional data. Despite the high accuracy it provides, in this case it lacks of interpretability when the set of rules is big. To address it, we aim, in this paper to optimize the number of rules using Genetic Algorithm (GA) by formulating a combinatorial optimization problem to minimize the ruleset and maximize the performance of the ruleset. We applied this approach on a real-life dataset involving slope stability, one of the most important subjects in civil engineering, by choosing random feature spaces to generate the rules. We also set a performance score that balances between the confidence and the support of the rules in a ruleset and that has to be maximized to select a ruleset as a potential candidate. The results obtained show that optimizing with GA gives a more powerful set of rules that eases the interpretation. However, if the goal of the study is to detect small groups, we should minimize the performance of the ruleset by looking at the weakest groups, hence with the lowest support.
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Nassih, R., Berrado, A. (2023). An Optimization Approach for Optimizing PRIM’s Randomly Generated Rules Using the Genetic Algorithm. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_23
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