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This paper aims to increase the diagnosis accuracy of the fault classification of power transformers by introducing a new off-line hybrid model based on a combination subset of the et method (C-set) & modified fuzzy C-mean algorithm (MFCM) and the optimizable multiclass-SVM (MCSVM). The innovation in this paper is shown in terms of solving the predicaments of outliers, boundary proportion, and unequal data existing in both traditional and intelligence models. Taking into consideration the closeness of dissolved gas analysis (DGA) data, the C-set method is implemented to subset the DGA data samples based on their type of faults within unrepeated subsets. Then, the MFCM is used for removing outliers from DGA samples by combining highly similar data for every subset within the same cluster to obtain the optimized training data (OTD) set. It is also used to minimize dimensionality of DGA samples and the uncertainty of transformer condition monitoring. After that, the optimized MCSVM is trained by using the (OTD). The proposed model diagnosis accuracy is 93.3%. The obtained results indicate that our model significantly improves the fault identification accuracy in power transformers when compared with other conventional and intelligence models.