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While subjective measurements are the most natural for assessing the user-perceived quality of a media stream, there are issues with their scalability and their context accuracy. We explore techniques to select application-layer measurements, collected by an instrumented media player, that most accurately predict the subjective quality rating that a user would assign to a stream. We consider three feature subset selection techniques that reduce the number of features (measurements) under consideration to ones most relevant to user-perceived stream quality. Two of the three techniques mathematically consider stream characteristics when selecting measurements, while the third is based on observation. We apply the reduced feature sets to two nearest-neighbor algorithms for predicting user-perceived stream quality. Our results demonstrate that there are clear strategies for estimating the quality rating that work well in specific circumstances such as video-on-demand services. The results also demonstrate that neither of the mathematically-based feature subset selection techniques identify a single set of features that is unambiguously influential on user-perceived stream quality, but that ultimately a combination of retransmitted and/or lost application-layer packets is most accurate for predicting stream quality.