Classifying Hierarchical Data Streams using Global Classifiers and Summarization Techniques

The hierarchical classification of data streams requires models capable of handling a class hierarchy and updating themselves whenever a new example arrives, within restrained processing time and memory consumption. Current state-of-the-art models store raw instances and handle the hierarchy locally, performing a high number of computations at every hierarchy level and with all, eventually redundant, data. This paper introduces Global k-Nearest Centroids (kNC) and Global Dribble, two novel methods for the hierarchical classification of data streams. Both methods use summarization techniques to represent data with constant computational resources usage and a global classification approach to process instances in less time when compared to local strategies. We compare both methods with a state-of-the-art local classifier, and the proposed methods achieved a higher number of correct predictions and process instances nearly twice as fast.