Abstract
We present an efficient method called ICE for incremental classification that employs tree-based sampling techniques and is independent of data distribution. The basic idea is to represent the class distribution in the dataset by using the weighted samples. The weighted samples are extracted from the nodes of intermediate decision trees using a clustering technique. As the data grows, an intermediate classifier is built only on the incremental portion of the data. The weighted samples from the intermediate classifier are combined with the previously generated samples to obtain an up-to-date classifier for the current data in an efficient, incremental fashion.
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Yoon, H., Alsabti, K., Ranka, S. (2001). Incremental Classification Using Tree-Based Sampling for Large Data. In: Liu, H., Motoda, H. (eds) Instance Selection and Construction for Data Mining. The Springer International Series in Engineering and Computer Science, vol 608. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3359-4_11
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DOI: https://doi.org/10.1007/978-1-4757-3359-4_11
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