Seasonal Averaged One-Dependence Estimators: a novel algorithm to address seasonal concept drift in high-dimensional stream classification

Rakshitha Godahewa, Trevor Yann, Christoph Bergmeir, Francois Petitjean

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world application data sources. Traditional approaches of stream classification consider seasonal drift by including seasonal dummy/indicator variables or building separate models for each season. But these approaches have strong limitations in high-dimensional classification problems, or with complex seasonal patterns. This paper explores how to best handle seasonal drift in the specific context of news article categorization (or classification/tagging), where seasonal drift is overwhelmingly the main type of drift present in the data, and for which the data are high-dimensional. We introduce a novel classifier named Seasonal Averaged One-Dependence Estimators (SAODE), which extends the AODE classifier to handle seasonal drift by including time as a super parent. We assess our SAODE model using two large real-world text mining related datasets each comprising approximately a million records, against nine state-of-the-art stream and concept drift classification models, with and without seasonal indicators and with separate models built for each season. Across five different evaluation techniques, we show that our model consistently outperforms other methods by a large margin where the results are statistically significant.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
EditorsAsim Roy
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728169262
ISBN (Print)9781728169279
DOIs
Publication statusPublished - 2020
EventIEEE International Joint Conference on Neural Networks 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020
https://ieeexplore.ieee.org/xpl/conhome/9200848/proceeding (Proceedings)
https://wcci2020.org/ijcnn-sessions/ (Website)

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2020
Abbreviated titleIJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20
Internet address

Keywords

  • averaged one-dependence estimators
  • high-dimensional stream classification
  • seasonal concept drift
  • seasonality

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