Evolutionary model selection in unsupervised learning

@article{Kim2002EvolutionaryMS,
  title={Evolutionary model selection in unsupervised learning},
  author={YongSeog Kim and William Nick Street and Filippo Menczer},
  journal={Intell. Data Anal.},
  year={2002},
  volume={6},
  pages={531-556}
}
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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