A Hierarchical Anytime k-NN Classifier for Large-Scale High-Speed Data Streams

Aarti, Jagat Sesh Challa, Hrishikesh Harsh, Utkarsh Darolia, Mansi Agarwal, Raghav Chaudhary, Navneet Goyal, Poonam Goyal

Abstract: The k-Nearest Neighbor Classifier (k-NN) is a widely used classification technique used in data streams. However, traditional k-NN-based stream classification algorithms can’t handle varying inter-arrival rates of objects in the streams. Anytime algorithms are a class of algorithms that effectively handle data streams that have variable stream speed and trade execution time with the quality of results. In this paper, we introduce a novel anytime k-NN classification method for data streams namely, ANY-k-NN. This method employs a proposed hierarchical structure, the Any-NN-forest, as its classification model. The Any-NN-forest maintains a hierarchy of micro-clusters with different levels of granularity in its trees. This enables ANY-k-NN to effectively handle variable stream speeds and incrementally adapt its classification model using incoming labeled data. Moreover, it can efficiently manage large data streams as the model construction is less expensive. It is also capable of handlin

External IDs:dblp:conf/icaart/AartiCHDACGG24