Learning evolving concepts using a partial memory approach

Marcus A. Maloof and Ryszard S. Michalski

This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example, building intelligent agents for helping users in Internet search, active vision, automatically updating knowledge-bases, or acquiring profiles of users of telecommunication networks. Requirements for a learning architecture supporting such applications include the ability to incrementally modify concept definitions to accommodate new information, fast learning and recognition rates, low memory needs, and the understandability of computer-created concept descriptions. To address these requirements, we propose a learning architecture based on Variable-Valued Logic, the Star Methodology, and the AQ algorithm. The method uses a partial-memory approach, which means that in each step of learning, the system remembers the current concept descriptions and specially selected representative examples from the past experience. The developed method has been experimentally applied to the problem of computer system intrusion detection. The results show significant advantages of the method in learning speed and memory requirements with only slight decreases in predictive accuracy and concept simplicity when compared to traditional batch-style learning in which all training examples are provided at once.

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Slides from the talk available in PostScript (gzipped) and PDF.

  author = "Maloof, M.A. and Michalski, R.S.",
  title = "Learning evolving concepts using a partial memory approach",
  booktitle = "{Working Notes of the AAAI Fall Symposium on Active Learning}",
  year = 1995,
  pages = "70--73",
  publisher = "AAAI Press",
  address = "Menlo Park, CA"