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| vigra::rf::algorithms Namespace Reference |

## Classes
class | ClusterImportanceVisitor
class | CorrectStatus
class | Draw
class | GetClusterVariables
struct | HC_Entry
class | HClustering
class | NormalizeStatus
class | PermuteCluster
class | RFErrorCallback
class | VariableSelectionResult
## Functions
template<class FeatureT , class ResponseT , class ErrorRateCallBack >
void | backward_elimination (FeatureT const &features, ResponseT const &response, VariableSelectionResult &result, ErrorRateCallBack errorcallback)
template<class FeatureT , class ResponseT >
void | cluster_permutation_importance (FeatureT const &features, ResponseT const &response, HClustering &linkage, MultiArray< 2, double > &distance)
template<class FeatureT , class ResponseT , class ErrorRateCallBack >
void | forward_selection (FeatureT const &features, ResponseT const &response, VariableSelectionResult &result, ErrorRateCallBack errorcallback)
template<class FeatureT , class ResponseT , class ErrorRateCallBack >
void | rank_selection (FeatureT const &features, ResponseT const &response, VariableSelectionResult &result, ErrorRateCallBack errorcallback)

## Detailed Description

This namespace contains all algorithms developed for feature selection

## Function Documentation

void vigra::rf::algorithms::forward_selection | ( | FeatureT const & | features,
|  | ResponseT const & | response,
|  | VariableSelectionResult & | result,
|  | ErrorRateCallBack | errorcallback
| ) |  |

Perform forward selection

Parameters:

  features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm
  response | IN: n x 1 matrix containing the corresponding response
  result | IN/OUT: VariableSelectionResult struct which will contain the results of the algorithm. Features between result.selected.begin() and result.pivot will be left untouched.

See also:
  VariableSelectionResult

Parameters:

  errorcallback | IN, OPTIONAL: Functor that returns the error rate given a set of features and labels. Default is the RandomForest OOB Error.

Forward selection subsequently chooses the next feature that decreases the Error rate most.

usage:

             MultiArray<2, double>     features = createSomeFeatures();
             MultiArray<2, int>        labels   = createCorrespondingLabels();
             VariableSelectionResult  result;
             forward_selection(features, labels, result);

To use forward selection but ensure that a specific feature e.g. feature 5 is always included one would do the following

             VariableSelectionResult result;
             result.init(features, labels);
             std::swap(result.selected[0], result.selected[5]);
             result.setPivot(1);
             forward_selection(features, labels, result);

See also:
  VariableSelectionResult

void vigra::rf::algorithms::backward_elimination | ( | FeatureT const & | features,
|  | ResponseT const & | response,
|  | VariableSelectionResult & | result,
|  | ErrorRateCallBack | errorcallback
| ) |  |

Perform backward elimination

Parameters:

  features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm
  response | IN: n x 1 matrix containing the corresponding response
  result | IN/OUT: VariableSelectionResult struct which will contain the results of the algorithm. Features between result.pivot and result.selected.end() will be left untouched.

See also:
  VariableSelectionResult

Parameters:

  errorcallback | IN, OPTIONAL: Functor that returns the error rate given a set of features and labels. Default is the RandomForest OOB Error.

Backward elimination subsequently eliminates features that have the least influence on the error rate

usage:

             MultiArray<2, double>     features = createSomeFeatures();
             MultiArray<2, int>        labels   = createCorrespondingLabels();
             VariableSelectionResult  result;
             backward_elimination(features, labels, result);

To use backward elimination but ensure that a specific feature e.g. feature 5 is always excluded one would do the following:

             VariableSelectionResult result;
             result.init(features, labels);
             std::swap(result.selected[result.selected.size()-1], result.selected[5]);
             result.setPivot(result.selected.size()-1);
             backward_elimination(features, labels, result);

See also:
  VariableSelectionResult

void vigra::rf::algorithms::rank_selection | ( | FeatureT const & | features,
|  | ResponseT const & | response,
|  | VariableSelectionResult & | result,
|  | ErrorRateCallBack | errorcallback
| ) |  |

Perform rank selection using a predefined ranking

Parameters:

  features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm
  response | IN: n x 1 matrix containing the corresponding response
  result | IN/OUT: VariableSelectionResult struct which will contain the results of the algorithm. The struct should be initialized with the predefined ranking.

See also:
  VariableSelectionResult

Parameters:

  errorcallback | IN, OPTIONAL: Functor that returns the error rate given a set of features and labels. Default is the RandomForest OOB Error.

Often some variable importance, score measure is used to create the ordering in which variables have to be selected. This method takes such a ranking and calculates the corresponding error rates.

usage:

             MultiArray<2, double>     features = createSomeFeatures();
             MultiArray<2, int>        labels   = createCorrespondingLabels();
             std::vector<int>        ranking  = createRanking(features);
             VariableSelectionResult  result;
             result.init(features, labels, ranking.begin(), ranking.end());
             backward_elimination(features, labels, result);

See also:
  VariableSelectionResult

void vigra::rf::algorithms::cluster_permutation_importance | ( | FeatureT const & | features,
|  | ResponseT const & | response,
|  | HClustering & | linkage,
|  | MultiArray< 2, double > & | distance
| ) |  |

Perform hierarchical clustering of variables and assess importance of clusters

Parameters:

  features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm
  response | IN: n x 1 matrix containing the corresponding response
  linkage | OUT: Hierarchical grouping of variables.
  distance | OUT: distance matrix used for creating the linkage

Performs Hierarchical clustering of variables. And calculates the permutation importance measures of each of the clusters. Use the Draw functor to create human readable output The cluster-permutation importance measure corresponds to the normal permutation importance measure with all columns corresponding to a cluster permuted. The importance measure for each cluster is stored as the status() field of each clusternode

See also:
  HClustering

usage:

             MultiArray<2, double>     features = createSomeFeatures();
             MultiArray<2, int>        labels   = createCorrespondingLabels();
             HClustering                linkage;
             MultiArray<2, double>    distance;
             cluster_permutation_importance(features, labels, linkage, distance)
            // create graphviz output

          Draw<double, int> draw(features, labels, "linkagetree.graph");
          linkage.breadth_first_traversal(draw);

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)