We study the tree induction over a stream of perennial objects. The perennial objects are dynamic in nature and cannot be forgotten. The objects come from a multi-table stream, e.g., streams of Customer and Transaction. As the Transactions arrive, the perennial Customers' profiles grow and accumulate over time. To perform tree induction, we propose a tree induction algorithm that can handle perennial objects. The algorithm also encompasses a method that identifies and adapts to the concept drift in the stream. We have also incorporated a conventional classifier (kNN) at the leaves to further improve the classification accuracy of our algorithm. We have evaluated our method on a synthetic dataset and the PKDD Challenge 1999 dataset. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Siddiqui, Z. F., & Spiliopoulou, M. (2010). Tree induction over perennial objects. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6187 LNCS, pp. 640–657). https://doi.org/10.1007/978-3-642-13818-8_43
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