Abstract—In the study of cadre profiling issue, the selection of label attributes poses the most significant challenge. In this paper, Rough Set is utilized to model the cadre profiling issue. Label selection issue, which stands for the difficulty of cadre profiling, is transformed into an attribute reduction issue within the Rough Set framework. The paper presents a Genetic Algorithm, which integrates adaptive crossover and mutation probability, best individual mutation and random mutation to address the attribute reduction issue. Comparative analysis demonstrates that the proposed algorithm exhibits good classification accuracy, attribute reduction rate and overall performance. Finally, the application effect of the proposed cadre profiling method is illustrated through an example of cadre selection and appointment.

Keywords—Rough set, attribute reduction, genetic algorithm, cadre profiling

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Cite: You-Ping Liu, Jian-Gang Shen, "A Cadre Profiling Method Based on an Adaptive Two-Stage Mutation Genetic Attribute Reduction Algorithm," Journal of Software, vol. 19, no. 3, pp. 86-97, 2024.

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