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This paper addresses the problem of diagnosing the fault symptoms of power transformers with measurement originated uncertainties, which arise from the imprecision of samples (i.e. due to noises and outliers) and the effect of class imbalance (i.e. samples are unequally distributed between different fault types) in a training dataset used to identify different fault types. Two fuzzy support vector machine (FSVM) algorithms namely fuzzy c-means clustering-based FSVM (FCM-FSVM) and kernel fuzzy c-means clustering-based FSVM (KFCM-FSVM) have been applied in this paper to deal with any noises and outliers in training dataset. In order to reduce the effect of class imbalance in training dataset, two approaches including between-class weighting and random oversampling have been adopted and integrated with FCM-FSVM and KFCM-FSVM. The case studies show that KFCM-FSVM algorithm and its variants have consistent tendency to attain satisfied classification accuracy in transformer fault diagnosis using dissolved gas analysis (DGA) measurements.