In data stream mining, state-of-the-art machine learning algorithms for the classification task associate each event with a class belonging to a finite, devoid of structural dependencies and usually small, set of classes. However, there are more complex dynamic problems where the classes we want to predict make up a hierarchal structure. In this paper, we propose an incremental method for hierarchical classification of data streams. We experimentally show that our stream hierarchical classifier present advantages to the traditional online setting in three real-world problems related to entomology, ichthyology, and audio processing.
CITATION STYLE
Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. A. P. A. (2019). Towards hierarchical classification of data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11401 LNCS, pp. 314–322). Springer Verlag. https://doi.org/10.1007/978-3-030-13469-3_37
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