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Résumé : With the proliferation of traffic encryption, the need for application identification of given encrypted traffic using machine learning is growing for application-specific traffic management. However, there is not much research on the negative impact of classification delay and misclassification. In particular, an application generating bursty traffic such as video streaming causes congestion during the classification, which degrades QoE due to a considerable classification delay. In this paper, we propose a new mechanism called Progressive Slicing, which adaptively assigns the flow to multiple slices according to the state of the classifications. This new classification mechanism mitigates the classification delay by progressively isolating flows into the slices in the course of the classifications. In addition, multiple stages of classifications improve accuracy and enable more flexible application slicing based on the confidence scores. We evaluate the proposed method on multiple user scenarios on Android VMs. We show that this method improves search response time on Twitter by 35% compared to the slicing with a single classification even when the traffic of video applications coexists.