In graded multi-label classification (GMLC), each data can be assigned to multiple labels according to a degree of membership on an ordinal scale, and with respect to label relations. For example, in a movie catalog web page, a five stars action movie should be at least a one star suspense movie. Ignoring those relations can lead to inconsistent predictions, but if they are considered, then a prediction error for one label will be propagated to all related labels. Most of existing approaches either ignore label relations, or can learn only relations fitting a predefined imposed structure. This paper is motivated by the lack of a study analysing the compromise between handling label relations and limiting error propagation in GMLC, and by the fact that there is no known approach giving a control on that compromise to allow such a study. In this paper, a new meta-classifier with two main advantages is proposed for GMLC. Firstly, no predefined structure is imposed for learning label relations, and secondly, the meta-classifier is based on three measures giving control on the studied compromise. The studied compromise is analysed according to its impact on the classifier complexity and on hamming-loss evaluation measure. A comparison to three existing approaches shows that the proposed meta-classifier is competitive according to hamming-loss evaluation measure, and it is the most stable classifier according to hamming-loss standard deviation.