High Level Classification seeks to identify class patterns based not only on physical features, such as distances among objects. For such, one of the recent approaches used is based on complex networks and its' topological properties. This approach has as advantage the capability to discriminate between highly complex class structures. However, from previous studies, it is known that a combination of high and low level classifiers is better than the use of the classifiers separately. In this work, we propose the use of a stacking procedure to combine such classifiers. The main advantage, compared to the current procedure, is the removal of critical parameters, which have major influence on the results. Also, we propose two new measures to capture different global patterns from the classes complex networks. We performed experiments on five UCI datasets and a subset of the Million song Dataset. Our results indicate that the use of stacking is capable of obtaining equal or better results when compared to optimizing the critical parameters by cross-validation.