An Event Group Based Classification Framework for Multi-variate Sequential Data
Keywords: Multi-variate time series, Symbolic data mining, Pattern search, SAX motifs, X-of-N decision trees
AbstractDecision tree algorithms were not traditionally considered for sequential data classification, mostly because feature generation needs to be integrated with the modelling procedure in order to avoid a localisation problem. This paper presents an Event Group Based Classification (EGBC) framework that utilises an X-of-N (XoN) decision tree algorithm to avoid the feature generation issue during the classification on sequential data. In this method, features are generated independently based on the characteristics of the sequential data. Subsequently an XoN decision tree is utilised to select and aggregate useful features from various temporal and other dimensions (as event groups) for optimised classification. This leads the EGBC framework to be adaptive to sequential data of differing dimensions, robust to missing data and accommodating to either numeric or nominal data types. The comparatively improved outcomes from applying this method are demonstrated on two distinct areas – a text based language identification task, as well as a honeybee dance behaviour classification problem. A further motivating industrial problem – hot metal temperature prediction, is further considered with the EGBC framework in order to address significant real-world demands.
How to Cite
Sun, C., & Stirling, D. (2017). An Event Group Based Classification Framework for Multi-variate Sequential Data. Australasian Journal of Information Systems, 21. https://doi.org/10.3127/ajis.v21i0.1551
Selected Papers from the Australasian Conference on Data Mining (AusDM)
Copyright (c) 2017 Chao Sun, David Stirling
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