关键词: 多粒度粗糙集, 大学生贫困资助, 绝对约简, 粒度约简, 信息量

Abstract: In recent years, in order to ensure the family financial difficulties students successfully completed their studies, central and local governments have developed a variety of policies, including the subsidy policy for poor students in university. But will also face a problem, is how to judge whether students are poor students. There are many factors that affect the assessment of funding, leading to the existence of funding assessment is not fair. In order to help the major colleges and universities to do a better job of poor student funding work, firstly, this paper designs the questionnaire of the college students’ poverty support assessment standard, to collect data from college freshmen to seniors. Then, taking advantage of the improved rough set theory which based on the pessimistic multi-granularity information reduction algorithm, optimistic multi-granularity reduction algorithm and the two-layer absolute granularity reduction algorithm, it has excavated the key factors that affect the evaluation criteria of poor students, and verified the correctness of the results by experiments. The research results can make the poverty assessment standard more just, so that poor students can better finish their studies under the assistance of the state, and lay a good foundation for future life.

Key words: multi granularity rough sets, college students poverty aid, absolution reduction, particle size reduction, amount of information