抄録
A new approach to DEA-based decision making is proposed in this paper, in which 2-D visual feature maps are constructed for intuitively assessing efficiencies of DMUs (decision making units) by using results of data envelopment analysis (DEA). In order to summarize intrinsic information of DEA results efficiently, several variants of principal component analysis (PCA) such as fuzzy PCA or PCA with variable selection are adopted. With the goal of clear identification of target efficient DMUs, the feature map is constructed by emphasizing the mutual relation among efficient DMUs where relatively larger fuzzy memberships are assigned to them in fuzzy PCA. Variable selection mechanism is also applied in order to avoid the illegal influences of singular DMUs. The advantages of the proposed approach are demonstrated in a numerical experiment with a data set on Japanese prefectural activities.
