Dependance of critical dimension on learning machines and ranking methods

  title={Dependance of critical dimension on learning machines and ranking methods},
  author={Divya Suryakumar and Andrew H. Sung and Qingzhong Liu},
  journal={2012 IEEE 13th International Conference on Information Reuse & Integration (IRI)},
Feature reduction is a major problem in data mining. Though traditional methods such as feature ranking and subset selection have been widely used, there has been little consideration given to assuring satisfactory performance of a learning machine in relation to the minimum of features required or the “critical dimension”. This critical dimension is unique to a specific dataset, learning machine, and ranking algorithm combination. The empirical methods demonstrate that many datasets show the… CONTINUE READING

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