select_feature_set_mlpT_select_feature_set_mlpSelectFeatureSetMlpSelectFeatureSetMlpselect_feature_set_mlp (Operator)

Name

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

Signature

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'"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_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

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