Adaptive Global k-Nearest Neighbors for Hierarchical Classification of Data Streams

Data stream classification differs from batch learning classification methods as data is made available sequentially and may drift over time. Therefore, data stream classification can be simultaneous to all other kinds of classification problems, and it has been revisiting many aspects related to classification in the last years. So far, hierarchical classification was weakly addressed in streaming scenarios despite being a well-established research topic. To fill in this gap between such areas, in this paper, we propose the adaptive global k-Nearest Neighbors for the hierarchical classification of data streams (Global kNN-hDS). Our proposal classifies hierarchical data streams using a constrained memory buffer and a global classification approach. We compare our method against a state-of-the-art local kNN also tailored for streaming scenarios, and results show that our method obtains competitive prediction rates while being statistically faster.