Classifying Potentially Unbounded Hierarchical Data Streams with Incremental Gaussian Naive Bayes

• Eduardo Tieppo PUCPR / IFPR https://orcid.org/0000-0001-8271-0214
• Jean Paul Barddal PUCPR https://orcid.org/0000-0001-9928-854X
• Júlio Cesar Nievola PUCPR https://orcid.org/0000-0002-2212-4499

Resumo

Hierarchical data stream classification inherits the properties and constraints of hierarchical classification and data stream classification concomitantly. Therefore, it requires novel approaches that (i) can handle class hierarchies, (ii) can be updated over time, and (iii) are computationally light-weighted regarding processing time and memory usage. In this study, we propose the Gaussian Naive Bayes for Hierarchical Data Streams (GNB-hDS) method: an incremental Gaussian Naive Bayes for classifying potentially unbounded hierarchical data streams. GNB-hDS uses statistical summaries of the data stream instead of storing actual instances. These statistical summaries allow more efficient data storage, keep constant computational time and memory, and calculate the probability of an instance belonging to a specific class via the Bayes’ Theorem. We compare our method against a technique that stores raw instances, and results show that our method obtains equivalent prediction rates while being significantly faster.

Palavras-chave: Hierarchical classification, Data stream classification, Gaussian Naive Bayes, Incremental learning