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This paper presents a novel approach to efficiently perform early system level design space exploration (DSE) of MultiProcessor System-on-Chip (MPSoC) based embedded systems. By modeling dynamic multi-application workloads using application scenarios, optimal designs can be quickly identified using a combination of a scenario-based DSE and a feature selection algorithm. The feature selection algorithm identifies a representative subset of scenarios, which is used to predict the fitness of the MPSoC design instances in the genetic algorithm of the scenario-based DSE. Results show that our scenario-based DSE provides a tradeoff between the speed and accuracy of the early DSE.