Evolutionary model selection in unsupervised learning

  title={Evolutionary model selection in unsupervised learning},
  author={YongSeog Kim and William Nick Street and Filippo Menczer},
  journal={Intell. Data Anal.},
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and possibly, accuracy of the resulting models. Feature selection has traditionally been studied in supervised learning situations, with some estimate of accuracy used to evaluate candidate subsets. However, we often cannot apply supervised learning for lack of a training signal. For these cases, we propose a new feature… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 55 references

CoIL challenge 2000: Choosing and explaining likely caravan insurance customers

Y. Kim, W. N. Street
Technical Report 2000-09, Sentient Machine Research and Leiden Institute of Advanced Computer Science, • 2000
View 2 Excerpts


F. Menczer
Degeratu and W.N. Street, Efficient and scalable pareto optimization by evolutionary local selection algorithms.Evolutionary Computation8(2) • 2000
View 2 Excerpts