Selecting Examples for Partial Memory Learning

  • Authors:

  • Marcus A. Maloof;Ryszard S. Michalski

  • Affiliations:

  • Department of Computer Science, Georgetown University, Washington, DC 20057, USA. maloof@cs.georgetown.edu;Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA&semi/ Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland. michalski@gmu.e ...

  • Venue:
  • Machine Learning
  • Year:
  • 2000

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Abstract

This paper describes a method for selecting training examples for a partial memory learning system. The method selects extreme examples that lie at the boundaries of concept descriptions and uses these examples with new training examples to induce new concept descriptions. Forgetting mechanisms also may be active to remove examples from partial memory that are irrelevant or outdated for the learning task. Using an implementation of the method, we conducted a lesion study and a direct comparison to examine the effects of partial memory learning on predictive accuracy and on the number of training examples maintained during learning. These experiments involved the STAGGER Concepts, a synthetic problem, and two real-world problems: a blasting cap detection problem and a computer intrusion detection problem. Experimental results suggest that the partial memory learner notably reduced memory requirements at the slight expense of predictive accuracy, and tracked concept drift as well as other learners designed for this task.