Advances in Instance Selection for Instance-Based Learning Algorithms

  title={Advances in Instance Selection for Instance-Based Learning Algorithms},
  author={Henry Brighton and Chris Mellish},
  journal={Data Mining and Knowledge Discovery},
The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically… CONTINUE READING
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