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Classifier evaluation (feature definition, feature size curve)


 A Training dataset.
 FEATDEF Untrained mapping(s), defining the feature space  (possibly cell array)
 CLASSF Untrained classifier(s) to be tested (possibly cell array)
 FEATSIZES Vector of feature sizes (default: all sizes)
 LEARNSIZE Number of objects/fraction of training set size  (see GENDAT)
 NREPS Number of repetitions (default: 1)
 S Independent test dataset (optional)
 TESTFUN Mapping,evaluation function (default classification error)

 E Cell array [#FEATDEF,#CLASSF] with results (structures)  See PLOTE for a description


This routine is an extension of CLEVALF which makes a feature curve of a  given dataset. CLEVALFS produces feature curves for every feature  representation defined in one of the cells of FEATDEF (e.g. based on PCAM or FEATSELI). The result is a set of feature curves that can be plotted  by PLOTE.

The following steps are taken

  • Split data dataset A in a trainset and and testset S according to  LEARNSIZE.
  • Compute for an element in FEATDEF a feature representation. This  includes a ranking of the features.
  • Compute for this set of features and one of the classifiers in CLASSF a  feature curve for the feature sizes as defined by FEATSIZES.
  • repeat the last line for all classifiers in CLASSF.
  • repeat the last three lines for all feature definitions in FEATDEF.
  • Store all results as cells in E.

This function uses the RAND random generator and thereby reproduces only  if its seed is saved and reset.

See also

mappings, datasets, cleval, clevalf, testc, plote, gendat,

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PRTools User Guide

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