Name
select_feature_set_knnT_select_feature_set_knnSelectFeatureSetKnnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnn — Selects an optimal subset from a set of features to solve a certain
classification problem.
Herror select_feature_set_knn(const HTuple& ClassTrainDataHandle, const HTuple& SelectionMethod, const HTuple& GenParamNames, const HTuple& GenParamValues, HTuple* KNNHandle, HTuple* SelectedFeatureIndices, HTuple* Score)
HTuple HClassKnn::SelectFeatureSetKnn(const HClassTrainData& ClassTrainDataHandle, const HTuple& SelectionMethod, const HTuple& GenParamNames, const HTuple& GenParamValues, HTuple* Score)
HClassKnn HClassTrainData::SelectFeatureSetKnn(const HTuple& SelectionMethod, const HTuple& GenParamNames, const HTuple& GenParamValues, HTuple* SelectedFeatureIndices, HTuple* Score) const
void SelectFeatureSetKnn(const HTuple& ClassTrainDataHandle, const HTuple& SelectionMethod, const HTuple& GenParamNames, const HTuple& GenParamValues, HTuple* KNNHandle, HTuple* SelectedFeatureIndices, HTuple* Score)
HTuple HClassKnn::SelectFeatureSetKnn(const HClassTrainData& ClassTrainDataHandle, const HString& SelectionMethod, const HTuple& GenParamNames, const HTuple& GenParamValues, HTuple* Score)
HTuple HClassKnn::SelectFeatureSetKnn(const HClassTrainData& ClassTrainDataHandle, const HString& SelectionMethod, const HString& GenParamNames, double GenParamValues, HTuple* Score)
HTuple HClassKnn::SelectFeatureSetKnn(const HClassTrainData& ClassTrainDataHandle, const char* SelectionMethod, const char* GenParamNames, double GenParamValues, HTuple* Score)
HClassKnn HClassTrainData::SelectFeatureSetKnn(const HString& SelectionMethod, const HTuple& GenParamNames, const HTuple& GenParamValues, HTuple* SelectedFeatureIndices, HTuple* Score) const
HClassKnn HClassTrainData::SelectFeatureSetKnn(const HString& SelectionMethod, const HString& GenParamNames, double GenParamValues, HTuple* SelectedFeatureIndices, HTuple* Score) const
HClassKnn HClassTrainData::SelectFeatureSetKnn(const char* SelectionMethod, const char* GenParamNames, double GenParamValues, HTuple* SelectedFeatureIndices, HTuple* Score) const
void HOperatorSetX.SelectFeatureSetKnn(
[in] VARIANT ClassTrainDataHandle, [in] VARIANT SelectionMethod, [in] VARIANT GenParamNames, [in] VARIANT GenParamValues, [out] VARIANT* KNNHandle, [out] VARIANT* SelectedFeatureIndices, [out] VARIANT* Score)
VARIANT HClassKnnX.SelectFeatureSetKnn(
[in] IHClassTrainDataX* ClassTrainDataHandle, [in] BSTR SelectionMethod, [in] VARIANT GenParamNames, [in] VARIANT GenParamValues, [out] VARIANT* Score)
IHClassKnnX* HClassTrainDataX.SelectFeatureSetKnn(
[in] BSTR SelectionMethod, [in] VARIANT GenParamNames, [in] VARIANT GenParamValues, [out] VARIANT* SelectedFeatureIndices, [out] VARIANT* Score)
static void HOperatorSet.SelectFeatureSetKnn(HTuple classTrainDataHandle, HTuple selectionMethod, HTuple genParamNames, HTuple genParamValues, out HTuple KNNHandle, out HTuple selectedFeatureIndices, out HTuple score)
HTuple HClassKnn.SelectFeatureSetKnn(HClassTrainData classTrainDataHandle, string selectionMethod, HTuple genParamNames, HTuple genParamValues, out HTuple score)
HTuple HClassKnn.SelectFeatureSetKnn(HClassTrainData classTrainDataHandle, string selectionMethod, string genParamNames, double genParamValues, out HTuple score)
HClassKnn HClassTrainData.SelectFeatureSetKnn(string selectionMethod, HTuple genParamNames, HTuple genParamValues, out HTuple selectedFeatureIndices, out HTuple score)
HClassKnn HClassTrainData.SelectFeatureSetKnn(string selectionMethod, string genParamNames, double genParamValues, out HTuple selectedFeatureIndices, out HTuple score)
select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnn selects an optimal subset from a set of
features to solve a certain classification problem.
The classification problem has to be specified with annotated training data
in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle and will be classified by a
a k-nearest neighbors classifier. Details of the properties of this
classifier can be found in create_class_knncreate_class_knnCreateClassKnncreate_class_knnCreateClassKnnCreateClassKnn.
The result of the operator is a trained classifier that is returned in
KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleKNNHandle. Additionally, the list of indices or names of
the selected features
is returned in SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices. To use this classifier,
calculate for new input data all features mentioned in
SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices 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 property that is discriminating between
different classes of parts or classes of errors.
To define the features that should be selected from
ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle, the dimensions of the
feature vectors in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle can be grouped into
subfeatures by calling set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData.
A subfeature can contain several subsequent elements of a feature vector.
The operator 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
SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndices.
If names were set in set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData, these
names are returned instead of the indices.
If set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData was not called for
ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle before, each element of the feature vector
is considered as a subfeature.
The selection method
SelectionMethodSelectionMethodSelectionMethodSelectionMethodSelectionMethodselectionMethod is either a greedy search 'greedy'"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""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""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""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 ScoreScoreScoreScoreScorescore.
The k-NN classifier can be parameterized using the following values in
GenParamNamesGenParamNamesGenParamNamesGenParamNamesGenParamNamesgenParamNames and GenParamValuesGenParamValuesGenParamValuesGenParamValuesGenParamValuesgenParamValues:
- 'num_neighbors'"num_neighbors""num_neighbors""num_neighbors""num_neighbors""num_neighbors":
-
The number of minimally evaluated
nodes, increase this value for high dimensional data.
Possible values: '1'"1""1""1""1""1", '2'"2""2""2""2""2", '5'"5""5""5""5""5",
'10'"10""10""10""10""10"
Default value: '1'"1""1""1""1""1"
- 'num_trees'"num_trees""num_trees""num_trees""num_trees""num_trees":
-
Number of search trees in the k-NN
classifier
Possible values: '1'"1""1""1""1""1", '4'"4""4""4""4""4", '10'"10""10""10""10""10"
Default value: '4'"4""4""4""4""4"
This operator may take considerable time, depending on the size of the
data set in the training file, 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_knnselect_feature_set_knnSelectFeatureSetKnnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnn may deliver a classifier with
a very high score. However, the classifier may perfom poorly when tested.
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Automatically parallelized on internal data level.
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.
Handle of the training data.
Method to perform the selection.
Default value:
'greedy'
"greedy"
"greedy"
"greedy"
"greedy"
"greedy"
List of values: 'greedy'"greedy""greedy""greedy""greedy""greedy", 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating"
Names of generic parameters to configure the
selection process and the classifier.
Default value: []
List of values: 'num_neighbors'"num_neighbors""num_neighbors""num_neighbors""num_neighbors""num_neighbors", 'num_trees'"num_trees""num_trees""num_trees""num_trees""num_trees"
Values of generic parameters to configure the
selection process and the classifier.
Default value: []
Suggested values: 1, 2, 3
A trained k-NN classifier using only the selected
features.
The selected feature set, contains
indices or names.
The achieved score using two-fold cross-validation.
* 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_sample_class_train_data (ClassTrainDataHandle, 'row', [0,0,1, 5,6 ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,3,2, 5,6 ], 1)
* Add more data
* ...
* Select the better feature with the k-NN classifier
select_feature_set_knn (ClassTrainDataHandle, 'greedy', [], [], KNNHandle,\
SelectedFeatureKNN, Score)
clear_class_train_data (ClassTrainDataHandle)
* Use the classifier
* ...
clear_class_knn (KNNHandle)
If the parameters are valid, the operator select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnn
returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData,
add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainData,
set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData
classify_class_knnclassify_class_knnClassifyClassKnnclassify_class_knnClassifyClassKnnClassifyClassKnn
select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlp,
select_feature_set_svmselect_feature_set_svmSelectFeatureSetSvmselect_feature_set_svmSelectFeatureSetSvmSelectFeatureSetSvm,
select_feature_set_gmmselect_feature_set_gmmSelectFeatureSetGmmselect_feature_set_gmmSelectFeatureSetGmmSelectFeatureSetGmm
select_feature_set_trainf_knnselect_feature_set_trainf_knnSelectFeatureSetTrainfKnnselect_feature_set_trainf_knnSelectFeatureSetTrainfKnnSelectFeatureSetTrainfKnn,
gray_featuresgray_featuresGrayFeaturesgray_featuresGrayFeaturesGrayFeatures,
region_featuresregion_featuresRegionFeaturesregion_featuresRegionFeaturesRegionFeatures
Foundation