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Incremental rule learning with partial instance memory for changing concepts

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1 Author(s)
M. A. Maloof ; Dept. of Comput. Sci., Georgetown Univ., Washington, DC, USA

Learning concepts that change over time is important for a variety of applications in which an intelligent system must acquire and use a behavioral profile. Computer intrusion detection, calendar scheduling, and intelligent user interfaces are three examples. An interesting class of methods for learning such concepts consists of algorithms that maintain a portion of previously encountered examples. Since concepts change over time and these methods store selected examples, mechanisms must exist to identify and remove irrelevant examples of old concepts. In this paper, we describe an incremental rule learner with partial instance memory, called AQ 11 -PM+WAH, that uses Widmer and Kubat's heuristic to adjust dynamically the window over which it retains and forgets examples. We evaluated this learner using the STAGGER concepts and made direct comparisons to AQ-PM and to AQ 11 - PM, similar learners with partial instance memory. Results suggest that the forgetting heuristic is not restricted to FLORA2 the learner for which it was originally designed. Overall, result from this study and others suggest learners with partial instance memory converge more quickly to changing target concepts than algorithms that learn solely from new examples.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003