Applicability of feature selection on multivariate time series data for robotic discovery

@article{Cheema2010ApplicabilityOF,
  title={Applicability of feature selection on multivariate time series data for robotic discovery},
  author={Shahzad Cheema and T. Henne and Uwe K{\"o}ckemann and E. Prassler},
  journal={2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)},
  year={2010},
  volume={2},
  pages={V2-592-V2-597}
}
Open ended robotic discovery aims at enabling robots to autonomously design and execute sophisticated experiments for gaining conceptual insight about real world. Such experiments are planned activities rather than innate motor commands and thus each single experiment results in a multivariate time series. In such a scenario, reducing the number of features in order to allow a symbolic learner to build a correct conceptual model of underlying phenomena is a fundamental task. Only few feature… Expand
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