Abstract
A new research frontier in AI and data mining seeks to develop methods to automatically discover relevant variables among many irrelevant ones. In this paper, we present four algorithms that output such crucial variables in PAC model with membership queries. The first algorithm executes the task under any unknown distribution by measuring the distance between virtual and real targets. The second algorithm exhausts virtual version space under an arbitrary distribution. The third algorithm exhausts universal set under the uniform distribution. The fourth algorithm measures influence of variables under the uniform distribution. Knowing the number r of relevant variables, the first algorithm runs in almost linear time for r. The second and the third ones use less membership queries than the first one, but runs in time exponential for r. The fourth one enumerates highly influential variables in quadratic time for r.
Author supported in part by the EC through the Esprit Program EU BRA program under project 20244 (ALCOM-IT) and the EC Working Group EP27150 (NeuroColt II) and by the spanish DGES PB95-0787 (Koala).
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Guijarro, D., Tarui, J., Tsukiji, T. (1999). Finding Relevant Variables in PAC Model with Membership Queries. In: Watanabe, O., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1999. Lecture Notes in Computer Science(), vol 1720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46769-6_26
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DOI: https://doi.org/10.1007/3-540-46769-6_26
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