AttributeNets: an incremental learning method for interpretable classification

Authors:
Hu Wu;Yongji Wang;Xiaoyong Huai
Affiliations:
Institute of Software, Chinese Academy of Sciences, Beijing, China and Graduate University of the Chinese Academy of Sciences, Beijing, China;Institute of Software, Chinese Academy of Sciences, Beijing, China;Institute of Software, Chinese Academy of Sciences, Beijing, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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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, Attribute Nets, which is efficient both in memory utilization and updating cost of current hypothesis. Attribute Nets 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, Attribute Nets could generate effective results interpretable to human beings.