Abstract
Incremental learning is of more and more importance in real world data mining scenarios. Memory cost and adaptation cost are two major concerns of incremental learning algorithms. In this paper we provide a novel incremental learning method, AttributeNets, which is efficient both in memory utilization and updating cost of current hypothesis. AttributeNets is designed for addressing incremental classification problem. Instead of memorizing every detail of historical cases, the method only records statistical information of attribute values of learnt cases. For classification problem, AttributeNets could generate effective results interpretable to human beings.
Support by National Nature Science Foundation of China(Grant Number 60372053).
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Wu, H., Wang, Y., Huai, X. (2007). AttributeNets: An Incremental Learning Method for Interpretable Classification. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_105
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DOI: https://doi.org/10.1007/978-3-540-71701-0_105
Publisher Name: Springer, Berlin, Heidelberg
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