select_feature_set_mlpT_select_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp (Operator)
select_feature_set_mlpT_select_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp — Selects an optimal combination of features to classify the provided data.
Signature
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)
HTuple HClassMlp::SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const wchar_t* SelectionMethod, const wchar_t* GenParamName, double GenParamValue, HTuple* Score)
(
Windows only)
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
HClassMlp HClassTrainData::SelectFeatureSetMlp(const wchar_t* SelectionMethod, const wchar_t* GenParamName, double GenParamValue, HTuple* SelectedFeatureIndices, HTuple* Score) const
(
Windows only)
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_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp 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 ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle and will be classified by a
Multilayer Perceptron. Details of the properties of this
classifier can be found in create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpcreate_class_mlp.
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 SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndicesselected_feature_indices. To use this classifier,
calculate for new input data all features mentioned in
SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndicesselected_feature_indices 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
ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle, the dimensions of the
feature vectors in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle can be grouped into
subfeatures by calling set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data.
A subfeature can contain several subsequent elements of a feature vector.
select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp 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
SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndicesselected_feature_indices.
If names were set in set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data, these
names are returned instead of the indices.
If set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data was not called for
ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle before, each element of the feature vector
is considered as a subfeature.
The selection method
SelectionMethodSelectionMethodSelectionMethodselectionMethodselection_method 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 GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value the number
of hidden layer can be set by
'num_hidden'"num_hidden""num_hidden""num_hidden""num_hidden". The default value is 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_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp may deliver a classifier with
a very high score. However, the classifier may perform poorly when tested.
Execution Information
- 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.
Parameters
ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle (input_control) class_train_data → HClassTrainData, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
Handle of the training data.
SelectionMethodSelectionMethodSelectionMethodselectionMethodselection_method (input_control) string → HTuplestrHTupleHtuple (string) (string) (HString) (char*)
Method to perform the selection.
Default:
'greedy'
"greedy"
"greedy"
"greedy"
"greedy"
List of values:
'greedy'"greedy""greedy""greedy""greedy", 'greedy_oscillating'"greedy_oscillating""greedy_oscillating""greedy_oscillating""greedy_oscillating"
GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name (input_control) string(-array) → HTupleMaybeSequence[str]HTupleHtuple (string) (string) (HString) (char*)
Names of generic parameters to configure the
selection process and the classifier.
Default:
[]
List of values:
'num_hidden'"num_hidden""num_hidden""num_hidden""num_hidden"
GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control) number(-array) → HTupleMaybeSequence[Union[int, str, float]]HTupleHtuple (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:
[]
Suggested values:
50, 80, 100
MLPHandleMLPHandleMLPHandleMLPHandlemlphandle (output_control) class_mlp → HClassMlp, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
A trained MLP classifier using only the selected
features.
SelectedFeatureIndicesSelectedFeatureIndicesSelectedFeatureIndicesselectedFeatureIndicesselected_feature_indices (output_control) string-array → HTupleSequence[str]HTupleHtuple (string) (string) (HString) (char*)
The selected feature set, contains
indices referring.
ScoreScoreScorescorescore (output_control) real-array → HTupleSequence[float]HTupleHtuple (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)
* Use the classifier
* ...
Result
If the parameters are valid, the operator select_feature_set_mlpselect_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp
returns the value 2 (
H_MSG_TRUE)
. If necessary, an exception is raised.
Possible Predecessors
create_class_train_datacreate_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data,
add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataadd_sample_class_train_data,
set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data
Possible Successors
classify_class_mlpclassify_class_mlpClassifyClassMlpClassifyClassMlpclassify_class_mlp
Alternatives
select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn,
select_feature_set_svmselect_feature_set_svmSelectFeatureSetSvmSelectFeatureSetSvmselect_feature_set_svm,
select_feature_set_gmmselect_feature_set_gmmSelectFeatureSetGmmSelectFeatureSetGmmselect_feature_set_gmm
See also
select_feature_set_trainf_mlpselect_feature_set_trainf_mlpSelectFeatureSetTrainfMlpSelectFeatureSetTrainfMlpselect_feature_set_trainf_mlp,
gray_featuresgray_featuresGrayFeaturesGrayFeaturesgray_features,
region_featuresregion_featuresRegionFeaturesRegionFeaturesregion_features
Module
Foundation