针对气象观测数据采集目的性弱、数据冗余度较高以及观测数据区问化中单值较多、等价类划分精度低的问题,提出一种基于遗传算法的气象观测数据区间值属性约简算法(MOIvGA).首先,通过改进区间值相似度,使其能够同时适用于单值等价关系判断和区间值相似度分析;其次,通过改进自适应遗传算法,提高其收敛性;最后,通过仿真实验证明,相对于运行自适应遗传属性约简(AGAv)算法求解最优值,所提算法迭代代数减少了22代;在区间长度为1h降水分类中,基于依赖度的区间值决策表λ-约简(MOIvGA)平均分类准确率比RIvD算法提高了6.3％,对无雨的预测准确率提高了7.13％;同时约简后的属性子集显著提高了分类准确率.由此可见,MOIvGA在区间值气象观测数据分析中能够提高收敛速度以及分类准确率.%Aiming at the problems that the purpose of the meteorological observation data acquisition is weak,the redundancy of data is high,and the number of single values in the observation data interval is large,the precision of equivalence partitioning is low,an attribute reduction algorithm for Meteorological Observation data Interval-value based on Genetic Algorithm (MOIvGA) was proposed.Firstly,by improving the similarity degree of interval value,the proposed algorithm could be suitable for both single value equivalence relation judgment and interval value similarity analysis.Secondly,the convergence of the algorithm was improved by the improved adaptive genetic algorithm.Finally,the simulation experiments show that the number of the iterations of the proposed algorithm is reduced by 22,compared with the method which operated AGAv (Adaptive Genetic Attribute reduction) algorithm to solve the optimal value.In the time interval of 1 hour precipitation classification,the average classification accuracy of the MOIvGA ()t-Reduction in Interval-valued decision table based on Dependence) algorithm is 6.3％ higher than that of RIvD algorithm;the accuracy of no rain forecasting is increased by 7.13％;at the same time,the classification accuracy can be significantly impoved by the attribute subset received by operating the MOIvGA algorithm.Therefore,the MOIvGA algorithm can increase the convergence rate and the classification accuracy in the analysis of interval value meteorological observation data.
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