Abstract
This paper extends the belief decision tree learning method to an incremental mode where the tree structure could change when new data come. This so-called incremental belief decision tree is a new classification method able to learn new instances incrementally, by updating and restructuring an existing belief decision tree. The induced decision tree is originally built in a batch mode under an uncertain framework, by means of the belief function theory, then updated by incorporating instances in a one-by-one basis once a new training set is available.
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Trabelsi, S., Elouedi, Z., El Aroui, M. (2014). Incremental Induction of Belief Decision Trees in Averaging Approach. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8644. Springer, Cham. https://doi.org/10.1007/978-3-319-10073-9_39
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DOI: https://doi.org/10.1007/978-3-319-10073-9_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10072-2
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