| Citation: | GUO Qiang, ZHAO Jiaqi. Aircraft type prediction based on approximate rough set resolution classification algorithm[J]. Journal of Aerospace Power, 2023, 38（5）：1250-1258 doi: 10.13224/j.cnki.jasp.20210304
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As a traditional data classification algorithm, C4.5 Decision Tree has the advantages of simple calculation and high accuracy. Due to the fact that aircraft has many parameters and large amount of data, C4.5 Decision Tree requires multiple sequential scanning of continuous attribute values, but the classification time efficiency is low. To solve this problem, an approximate rough set resolution classification algorithm was proposed. Rough set approximation was used to determine the ability of attributes to divide sample data. According to the probability acquired, the attribute with the largest resolution was used as the split character to establish the classification decision tree. The algorithm improved the computational efficiency and reduced the generation of over-fitting problem while ensuring the accuracy of classification decision. Through comparative experimental analysis of multiple sets of data samples on UCI (University of California, Irvine) databases, it proved that the PSRP (rough set and resolving power) algorithm improved the average computational time efficiency by about 10% and reliability by 2% while ensuring the same accuracy.
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