{Resource Description Framework}{RDF} triples datasets are rapidly expanding with the development of {Linked Data}. Nowadays, these triples have exceeded 31 billion, but less than 2\% of triples are links between knowledge bases. The \emph{instance matching problem} aims to establish links between two instances defined with these triples in different datasets that describe the same real-world phenomenon. % why instance matching is important The implementation of instance matching in \emph{Semantic Web} datasets can benefit several application contexts, such as data cleaning and data integration. The Silk tool, a link discovery framework for the web of data, has become a relatively mature and complete tool to solve instance matching problem. % why silk is mature Since the Silk tool outperforms the state-of-the-art instance matching approaches in accuracy, it is taken as baseline approach in this paper. However, the baseline approach has two shortcomings, it does not cope with the \emph{aggregation problem} and \emph{aggregation threshold selection problem}.  % explain a little about the problem The aggregation problem refers to the difficulty encountered when an aggregated similarity value is used to represent the total of all individual property value similarity features. In this paper scope, this means that the baseline approach only considers a final aggregated value to evaluate the semantic similarity between two instances, but this may lead to the hidden intermediate similarity features,  further resulting in skewing the matching accuracy. While aggregation threshold selection problem means that a threshold should be given to be compared with the final aggregated value to judge the matching, but normally it is difficult to be given. Moreover, considering the aforementioned data volume, even with excellent success rate of the baseline approach, subtle improvements in the accuracy of the matching would involve solving millions of potential flaws. This paper contributes  with a hybrid machine learning approach, which not only overcomes the explained  limitations, but also improves the matching accuracy. This contribution is based on combining a classification model with the linkage rule constructed by the baseline Silk.