ClassesClasses | | Operators

select_feature_set_mlpT_select_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlp (Operator)

Name

select_feature_set_mlpT_select_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlp — Selects an optimal combination of features to classify the provided data.

Signature

select_feature_set_mlp( : : ClassTrainDataHandle, SelectionMethod, GenParamName, GenParamValue : MLPHandle, SelectedFeatureIndices, Score)

Herror T_select_feature_set_mlp(const Htuple ClassTrainDataHandle, const Htuple SelectionMethod, const Htuple GenParamName, const Htuple GenParamValue, Htuple* MLPHandle, Htuple* SelectedFeatureIndices, Htuple* Score)

void SelectFeatureSetMlp(const HTuple& ClassTrainDataHandle, const HTuple& SelectionMethod, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* MLPHandle, HTuple* SelectedFeatureIndices, HTuple* Score)

HTuple HClassMlp::SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const HString& SelectionMethod, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* Score)

HTuple HClassMlp::SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const HString& SelectionMethod, const HString& GenParamName, double GenParamValue, HTuple* Score)

HTuple HClassMlp::SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const char* SelectionMethod, const char* GenParamName, double GenParamValue, HTuple* Score)

HClassMlp HClassTrainData::SelectFeatureSetMlp(const HString& SelectionMethod, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* SelectedFeatureIndices, HTuple* Score) const

HClassMlp HClassTrainData::SelectFeatureSetMlp(const HString& SelectionMethod, const HString& GenParamName, double GenParamValue, HTuple* SelectedFeatureIndices, HTuple* Score) const

HClassMlp HClassTrainData::SelectFeatureSetMlp(const char* SelectionMethod, const char* GenParamName, double GenParamValue, HTuple* SelectedFeatureIndices, HTuple* Score) const

static void HOperatorSet.SelectFeatureSetMlp(HTuple classTrainDataHandle, HTuple selectionMethod, HTuple genParamName, HTuple genParamValue, out HTuple MLPHandle, out HTuple selectedFeatureIndices, out HTuple score)

HTuple HClassMlp.SelectFeatureSetMlp(HClassTrainData classTrainDataHandle, string selectionMethod, HTuple genParamName, HTuple genParamValue, out HTuple score)

HTuple HClassMlp.SelectFeatureSetMlp(HClassTrainData classTrainDataHandle, string selectionMethod, string genParamName, double genParamValue, out HTuple score)

HClassMlp HClassTrainData.SelectFeatureSetMlp(string selectionMethod, HTuple genParamName, HTuple genParamValue, out HTuple selectedFeatureIndices, out HTuple score)

HClassMlp HClassTrainData.SelectFeatureSetMlp(string selectionMethod, string genParamName, double genParamValue, out HTuple selectedFeatureIndices, out HTuple score)

Description

select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpSelectFeatureSetMlp selects an optimal subset from a set of features to solve a given classification problem. The classification problem has to be specified with annotated training data in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle and will be classified by a Multilayer Perceptron. Details of the properties of this classifier can be found in create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlp.

The result of the operator is a trained classifier that is returned in MLPHandleMLPHandleMLPHandleMLPHandleMLPHandle. Additionally, the list of indices or names of the selected features is returned in SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices. To use this classifier, calculate for new input data all features mentioned in SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices and pass them to the classifier.

A possible application of this operator can be a comparison of different parameter sets for certain feature extraction techniques. Another application is to search for a feature that is discriminating between different classes.

To define the features that should be selected from ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle, the dimensions of the feature vectors in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle can be grouped into subfeatures by calling set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData. A subfeature can contain several subsequent elements of a feature vector. select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpSelectFeatureSetMlp decides for each of these subfeatures, if it is better to use it for the classification or leave it out.

The indices of the selected subfeatures are returned in SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices. If names were set in set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData, these names are returned instead of the indices. If set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData was not called for ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle before, each element of the feature vector is considered as a subfeature.

The selection method SelectionMethodSelectionMethodSelectionMethodSelectionMethodselectionMethod is either a greedy search 'greedy'"greedy""greedy""greedy""greedy" (iteratively add the feature with highest gain) or the dynamically oscillating search 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating" (add the feature with highest gain and test then if any of the already added features can be left out without great loss). The method 'greedy'"greedy""greedy""greedy""greedy" is generally preferable, since it is faster. Only in cases when the subfeatures are low-dimensional or redundant, the method 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating" should be chosen.

The optimization criterion is the classification rate of a two-fold cross-validation of the training data. The best achieved value is returned in ScoreScoreScoreScorescore.

With GenParamNameGenParamNameGenParamNameGenParamNamegenParamName and GenParamValueGenParamValueGenParamValueGenParamValuegenParamValue the number of hidden layer can be set by 'num_hidden'"num_hidden""num_hidden""num_hidden""num_hidden". The default value is '80'"80""80""80""80". Larger values for this parameter lead to longer classification times, while it allows a more expressive classifier.

Attention

This operator may take considerable time, depending on the size of the data and the number of features.

Please note, that this operator should not be called, if only a small set of training data is available. Due to the risk of overfitting the operator select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpSelectFeatureSetMlp may deliver a classifier with a very high score. However, the classifier may perfom poorly when tested.

Execution Information

This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

Parameters

ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle (input_control)  class_train_data HClassTrainData, HTupleHTupleHtuple (integer) (IntPtr) (Hlong) (Hlong)

Handle of the training data.

SelectionMethodSelectionMethodSelectionMethodSelectionMethodselectionMethod (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Method to perform the selection.

Default value: 'greedy' "greedy" "greedy" "greedy" "greedy"

List of values: 'greedy'"greedy""greedy""greedy""greedy", 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating"

GenParamNameGenParamNameGenParamNameGenParamNamegenParamName (input_control)  string(-array) HTupleHTupleHtuple (string) (string) (HString) (char*)

Names of generic parameters to configure the selection process and the classifier.

Default value: []

List of values: 'num_hidden'"num_hidden""num_hidden""num_hidden""num_hidden"

GenParamValueGenParamValueGenParamValueGenParamValuegenParamValue (input_control)  number(-array) HTupleHTupleHtuple (real / integer / string) (double / int / long / string) (double / Hlong / HString) (double / Hlong / char*)

Values of generic parameters to configure the selection process and the classifier.

Default value: []

Suggested values: 50, 80, 100

MLPHandleMLPHandleMLPHandleMLPHandleMLPHandle (output_control)  class_mlp HClassMlp, HTupleHTupleHtuple (integer) (IntPtr) (Hlong) (Hlong)

A trained MLP classifier using only the selected features.

SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices (output_control)  string-array HTupleHTupleHtuple (string) (string) (HString) (char*)

The selected feature set, contains indices referring.

ScoreScoreScoreScorescore (output_control)  real-array HTupleHTupleHtuple (real) (double) (double) (double)

The achieved score using two-fold cross-validation.

Example (HDevelop)

* Find out which of the two features distinguishes two Classes
NameFeature1 := 'Good Feature'
NameFeature2 := 'Bad Feature'
LengthFeature1 := 3
LengthFeature2 := 2
* Create training data
create_class_train_data (LengthFeature1+LengthFeature2,\
  ClassTrainDataHandle)
* Define the features which are in the training data
set_feature_lengths_class_train_data (ClassTrainDataHandle, [LengthFeature1,\
  LengthFeature2], [NameFeature1, NameFeature2])
* Add training data
*                                                         |Feat1| |Feat2|
add_sample_class_train_data (ClassTrainDataHandle, 'row', [1,1,1,  2,1  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,2,2,  2,1  ], 1)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [1,1,1,  3,4  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,2,2,  3,4  ], 1)
* Add more data 
* ...
* Select the better feature with a MLP
select_feature_set_mlp (ClassTrainDataHandle, 'greedy', [], [], MLPHandle,\
  SelectedFeatureMLP, Score)
clear_class_train_data (ClassTrainDataHandle)
* Use the classifier
* ...
clear_class_mlp (MLPHandle)

Result

If the parameters are valid, the operator select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpSelectFeatureSetMlp returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Possible Predecessors

create_class_train_datacreate_class_train_dataCreateClassTrainDataCreateClassTrainDataCreateClassTrainData, add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataAddSampleClassTrainData, set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData

Possible Successors

classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpClassifyClassMlp

Alternatives

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnSelectFeatureSetKnn, select_feature_set_svmselect_feature_set_svmSelectFeatureSetSvmSelectFeatureSetSvmSelectFeatureSetSvm, select_feature_set_gmmselect_feature_set_gmmSelectFeatureSetGmmSelectFeatureSetGmmSelectFeatureSetGmm

See also

select_feature_set_trainf_mlpselect_feature_set_trainf_mlpSelectFeatureSetTrainfMlpSelectFeatureSetTrainfMlpSelectFeatureSetTrainfMlp, gray_featuresgray_featuresGrayFeaturesGrayFeaturesGrayFeatures, region_featuresregion_featuresRegionFeaturesRegionFeaturesRegionFeatures

Module

Foundation


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