计算机应用与软件
計算機應用與軟件
계산궤응용여연건
COMPUTER APPLICATIONS AND SOFTWARE
2014年
5期
264-267,294
,共5页
不完备信息系统%对象完备度%规则提取%决策树%数据填补
不完備信息繫統%對象完備度%規則提取%決策樹%數據填補
불완비신식계통%대상완비도%규칙제취%결책수%수거전보
Incomplete information systems%Object completeness%Rules extraction%Decision tree%Data imputation
不完备信息系统中决策规则的提取是数据挖掘领域的重要研究问题。对不完备信息系统中决策规则的主要获取方法进行分析,以决策属性具有缺失值的不完备决策表为研究对象,提出一种基于数据优先填补的决策树规则提取算法。针对ROUSTIDA算法在数据填补时运算量较大且容易导致决策规则冲突这一问题,算法采用决策属性优先填补的思想,引入对象完备度概念对其进行改进,使用改进的ROUSTIDA算法对不完备决策表进行一次性数据填补预处理,并在限制容差关系下采用属性重要性为启发函数构建决策树,从而获得决策规则。实例表明该方法是有效的,生成的决策规则简单,且具有较高的精确度。
不完備信息繫統中決策規則的提取是數據挖掘領域的重要研究問題。對不完備信息繫統中決策規則的主要穫取方法進行分析,以決策屬性具有缺失值的不完備決策錶為研究對象,提齣一種基于數據優先填補的決策樹規則提取算法。針對ROUSTIDA算法在數據填補時運算量較大且容易導緻決策規則遲突這一問題,算法採用決策屬性優先填補的思想,引入對象完備度概唸對其進行改進,使用改進的ROUSTIDA算法對不完備決策錶進行一次性數據填補預處理,併在限製容差關繫下採用屬性重要性為啟髮函數構建決策樹,從而穫得決策規則。實例錶明該方法是有效的,生成的決策規則簡單,且具有較高的精確度。
불완비신식계통중결책규칙적제취시수거알굴영역적중요연구문제。대불완비신식계통중결책규칙적주요획취방법진행분석,이결책속성구유결실치적불완비결책표위연구대상,제출일충기우수거우선전보적결책수규칙제취산법。침대ROUSTIDA산법재수거전보시운산량교대차용역도치결책규칙충돌저일문제,산법채용결책속성우선전보적사상,인입대상완비도개념대기진행개진,사용개진적ROUSTIDA산법대불완비결책표진행일차성수거전보예처리,병재한제용차관계하채용속성중요성위계발함수구건결책수,종이획득결책규칙。실례표명해방법시유효적,생성적결책규칙간단,차구유교고적정학도。
Decision rules extraction in incomplete information systems is an important issue to be studied in data mining field.We analyse the principal decision rules acquisition method in incomplete information system,and take the incomplete decision table with missing decision attribution values as the research object,propose a data imputation prior-based decision tree rules extraction algorithm.For the deficiency of ROUSTIDA algorithm that it has large amount of computation in data imputation and is easy to cause decision rule conflict,the algorithm adoptsthe idea of giving the imputation priority to decision attributes and introduces the concept of object completeness to improve it,and uses the improved ROUSTIDA algorithm for one-off preprocessing of data imputation on incomplete decision table,as well as employs attribute significancewhen in limited tolerance relation as the heuristic function to construct decision tree,so as to obtain the decision rule.Examples show that the method is effective,the generated decision rule is simple and has a higher accuracy.