Power system transient stability assessment (TSA) can be represented as a machine learning (ML) binary classification problem. Network measurements data, collected from the distributed phasor measurement units during disturbances, constitute a large and imbalanced data set, on which the ML can be applied in order to learn to recognize the loss-of-stability from the various TSA incidents. This dataset, for actual power networks, contains hundreds of features, many of which can possibly be redundant and/or multi-correlated. This paper proposes an ensemble learning approach to the TSA classification problem, which includes a diverse set of base learners united by a voting ensemble. The imbalanced sample distribution and unequal misclassification costs are considered. Proposed approach also considers a feature selection as a pre-processing step, which is based on the importance analysis from different decision trees based models. Proposed ensemble learning model is applied on the IEEE New England 39-bus test case system. The obtained simulation results corroborate excellent performance and robustness of the proposed approach.