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瀛歌鍫卞憡锛欴ynamic Data Stream Mining with Scarcity of Labels

鐧煎竷鏅傞枔锛�2024-10-24     鐎忚閲忥細娆�

鍫卞憡椤岀洰锛�Dynamic Data Stream Mining with Scarcity of Labels

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鍫卞憡鎽樿锛�Data stream mining is a natural and necessary progression from traditional data mining. However, it presents additional challenges to batch analysis: along with strict time and memory constraints, change is a major consideration. In a dynamic data stream, the underlying concepts may drift and change over time. The challenge of recognizing and reacting to change in a stream is compounded by the scarcity of labels problem. This talk presents our recent work to evaluate unsupervised learning as the basis for online classi?cation in dynamic data streams with a scarcity of labels. A novel stream clustering algorithm based on the collective behavior of ants, called Ant Colony Stream Clustering (ACSC), is present. Furthermore, a novel framework, Clustering and One class Classi?cation Ensemble Learning (COCEL), for classi?cation in dynamic streams with a scarcity of labels is described. The proposed framework can identify and react to change in a stream and hugely reduces the number of required labels (typically less than 0.05% of the entire stream). Finally, some conclusions will be made.

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