Classification rules are extracted from sample data known as knowledge. If we extract these knowledge in a distributed way, it is necessary to combine or fuse these rules. The task of data fusion is to identify the true values of data items among multiple observed values drawn from different sources of varying reliability. In data mining applications knowledge extraction is splitted into subtasks due to memory or run-time limitations. Again, locally extracted knowledge must be consolidated later because communication overhead should be low. Extracting information from multiple data sources, and reconciling the values so the true values can be stored in a central data repository. But it's a problem of vital importance to the database and knowledge management communities.In a conventional approach extracting knowledge is typically done either by combining the classifiers' outputs or by combining the sets of classification rules but in this paper, I introduce a new way of fusing classifiers at the level of parameters of classification rules. Here its focused around the utilization of probabilistic generative classifiers utilizing multinomial circulations and multivariate ordinary dispersions for the consistent ones. We are using these distributions as hyper distributions or second-order distributions. Fusing of these classifiers are can be done by multiplying the hyper-distributions of the parameters.