Knowledge Acquisition Methods for Multi-Label Decision System Based on Rough Sets
Yu Yin · Jisuanji kexue yu tansuo · 2015
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.