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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nguifo, E.M., Njiwoua, P.: IGLUE: A Lattice-based Constructive Induction System. Intelligent Data Analysis Journal 5(1), 73–81 (2001)
Enembreck, F., Barths, J.P.: ELA: A New Approach for Learning Agents. Journal of Autonomous Agents and Multi-Agent Systems 3(10), 215–248 (2005)
Godin, R.: Incremental Concept Formation Algorithm Based on Galois (Concept) Lattices. Computational Intelligence 11(2), 246–267 (1995)
Hu, K., Lu, Y., Shi, C.: Incremental discovering association rules: a concept lattice approach. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 109–113. Springer, Heidelberg (1999)
Utgoff, P.E.: Incremental Induction of Decision Trees. Machine Learning 4, 161–186 (1989)
Utgoff, P.E.: An Improved Algorithm for Incremental Induction of Decision Trees. In: Proceedings of the Eleventh International Conference of Machine Learning, pp. 318–325 (1994)
Maloof, M.: Incremental Rule Learning with Partial Instance Memory for Changing Concepts. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN ’03), Los Alamitos, CA, pp. 2764–2769 (2003)
Lange, S., Grieser, G.: On the Power of Incremental Learning. Theory Computer Science 288(2), 277–307 (2002)
Zheng, Z., Wang, G., Wu, Y.: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm. In: Wang, G., et al. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 122–129. Springer, Heidelberg (2003)
Thrun, S.B., et al.: The MONK’s Problems: A Performance Comparison of Different Learning Algorithms. Technical report. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA (1991)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-71701-0_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
eBook Packages: Computer ScienceComputer Science (R0)