StreamSoNG: A Soft Streaming Classification Approach
This work addresses streaming classification for data mining applications, but it is incremental as it builds on existing methods with a soft assignment modification.
The paper tackles the problem of crisp label assignments in streaming classification by proposing a soft streaming classification approach that uses Neural Gas prototypes and possibilistic label vectors, achieving excellent results compared to three existing streaming classifiers on synthetic and real image datasets.
Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned a crisp label (into one of the structures) and that structure's footprint is incrementally updated. There is no reason that these assignments need to be crisp. In this paper, we propose a new streaming classification algorithm that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (of typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm. The approach is tested on synthetic and real image datasets. We compare our approach to three other streaming classifiers based on the Adaptive Random Forest, Very Fast Decision Rules, and the DenStream algorithm with excellent results.