CAJ | 학술논문

在多标记决策系统中，每个对象由单个实例进行表示，同时对应于多个决策属性。粗糙集理论已有的研究工作主要集中在单一决策系统的研究上，对于多决策系统只是简单地将它分解成多个单一决策系统。直接变换的方法忽视了决策属性之间的相关性和共现性，影响决策的精度。基于粗糙集模型，分别针对属性值为离散型和连续型的情况，提出了离散型多标记决策系统知识获取算法DML和连续型多标记决策系统知识获取算法CML。这两种算法均考虑了标记之间的相关性，在离散多标记决策系统中，采用决策链方式传递属性间的相关性，而在连续多标记决策系统中，扩展了传统粗糙集模型，重新定义了粗糙近似。实验表明，不论是离散型还是连续型决策系统，考虑决策属性之间的相关性均可以提高预测的准确率。
재다표기결책계통중，매개대상유단개실례진행표시，동시대응우다개결책속성。조조집이론이유적연구공작주요집중재단일결책계통적연구상，대우다결책계통지시간단지장타분해성다개단일결책계통。직접변환적방법홀시료결책속성지간적상관성화공현성，영향결책적정도。기우조조집모형，분별침대속성치위리산형화련속형적정황，제출료리산형다표기결책계통지식획취산법DML화련속형다표기결책계통지식획취산법CML。저량충산법균고필료표기지간적상관성，재리산다표기결책계통중，채용결책련방식전체속성간적상관성，이재련속다표기결책계통중，확전료전통조조집모형，중신정의료조조근사。실험표명，불론시리산형환시련속형결책계통，고필결책속성지간적상관성균가이제고예측적준학솔。
Multi-label learning deals with the problem where each instance is represented by a feature vector while associated with multiple decision attributions. The existing research on rough sets focuses on decision system with single decision attribute. For the decision system with multiple decision attributes, it is simply converted into several single decision systems. One single decision system is built for one decision attribute, which neglects the correlation among the different decision attributions and reduces the classification accuracy. Based on rough sets, this paper pro-poses two decision-making algorithms DML and CML for discrete and continuous attributes respectively. These two algorithms consider the correlation between the labels. DML constructs a decision chain to deliver the correlation among decision attributes, while CML extends the traditional rough set model and redefines the upper and lower approximation. The experimental results show that both discrete and continuous multi-label decision systems which consider the correlation between decision attributes perform better than those algorithms which neglect the correlation among decision attributions.