基于粗糙集约简与概率图的认知诊断模型研究  

Research on Cognitive Diagnosis Model Based on Rough Set Reduction and Probability Graph

作　　者：李庆波 赵宇兰 张如静[3] LI Qingbo;ZHAO Yulan;ZHANG Rujing(Experimental Training Teaching Department,Shanxi Vocational University of Engineering Science and Technology,Jinzhong Shanxi 030619,China;College of Information Engineering,Shanxi Vocational University of Engineering Science and Technology,Jinzhong Shanxi 030619,China;School of Media Technology,Liaocheng University,Liaocheng Shandong 252059,China)

机构地区：[1]山西工程科技职业大学实验实训教学部,山西晋中030619 [2]山西工程科技职业大学信息工程学院,山西晋中030619 [3]聊城大学传媒技术学院,山东聊城252059

出　　处：《西南大学学报（自然科学版）》2024年第11期217-226,共10页Journal of Southwest University(Natural Science Edition)

基　　金：国家自然科学基金项目(62307022);山西工程科技职业大学教学改革项目(GKDXJ202317)。

摘　　要：随着孤独症儿童的数量不断增加,准确且及时地对其进行认知诊断变得愈发重要.构建基于粗糙集约简算法的孤独症诊断知识库,研究基于概率图的认知诊断模型,以提高对孤独症儿童诊断的准确性和效率.实验结果表明:该认知诊断模型的均方根误差值范围为0.10~0.11,平均绝对误差值范围为0.009~0.115,在孤独症儿童的认知诊断中具有较高的准确性和稳定性.As the number of children with autism continues to increase,accurate and timely cognitive diagnosis has become increasingly important.The diagnosis knowledge base of autism was constructed based on rough set reduction algorithm,and the cognitive diagnosis model was developed based on probability graph to improve the accuracy and efficiency of the diagnosis of autistic children.The experimental results show that the root mean square error value of the proposed model ranged from 0.10 to 0.11,and the average absolute error value ranged from 0.009 to 0.115.The cognitive diagnosis model has high accuracy and stability in the cognitive diagnosis of autistic children.

关 键 词：数据挖掘 孤独症 认知诊断模型 粗糙集约简 概率图 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]