INCREMENTAL AUGMENT ALGORITHM BASED ON REDUCED Q-MATRIX  

简化Q矩阵的渐增式扩张生成算法(英文)

作　　者：杨淑群[1,2] 丁树良[3] 丁秋林[2] 

机构地区：[1]福建师范大学软件学院 [2]南京航空航天大学信息科学与技术学院 [3]江西师范大学信息工程学院

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2010年第2期183-189,共7页南京航空航天大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (30860084,60673014,60263005);the Backbone Young Teachers Foundation of Fujian Normal University(2008100244);the Department of Education Foundation of Fujian Province (ZA09047)~~

摘　　要：Reduced Q-matrix （Qr matrix） plays an important role in the rule space model （RSM） and the attribute hierarchy method （AHM）. Based on the attribute hierarchy, a valid/invalid item is defined. The judgment method of the valid/invalid item is developed on the relation between reachability matrix and valid items. And valid items are explained from the perspective of graph theory. An incremental augment algorithm for constructing Qr matrix is proposed based on the idea of incremental forward regression, and its validity is theoretically considered. Results of empirical tests are given in order to compare the performance of the incremental augment algo-rithm and the Tatsuoka algorithm upon the running time. Empirical evidence shows that the algorithm outper-forms the Tatsuoka algorithm, and the analysis of the two algorithms also show linear growth with respect to the number of valid items. Mathematical models with 10 attributes are built for the two algorithms by the linear regression analysis.简化Q矩阵( Qr阵)是规则空间模型与属性层次方法的重要概念。基于属性层次结构,提出有效/无效项目的定义,研究属性层次结构的可达矩阵与有效项目之间的关系,给出有效/无效项目的判定定理。基于逐步向前回归的思想提出了求解Qr阵的渐增式扩张算法,给出相关理论依据。在考虑有效项目数的基础上,与Tatsuoka方法进行了实验比较,对属性个数为10的情况采用线性回归方法为两种方法建立了数学模型。

关 键 词：reduced Q-matrix（Qr matrix） valid items incremental augment algorithm linear regression 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]