select_feature_set_mlp — Selects an optimal combination of features to classify the provided data.
select_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
ClassTrainDataHandle and will be classified by a
Multilayer Perceptron. Details of the properties of this
classifier can be found in
The result of the operator is a trained classifier that is returned in
MLPHandle. Additionally, the list of indices or names of the
is returned in
SelectedFeatureIndices. To use this classifier,
calculate for new input data all features mentioned in
SelectedFeatureIndices 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
ClassTrainDataHandle, the dimensions of the
feature vectors in
ClassTrainDataHandle can be grouped into
subfeatures by calling
A subfeature can contain several subsequent elements of a feature vector.
select_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
If names were set in
names are returned instead of the indices.
set_feature_lengths_class_train_data was not called for
ClassTrainDataHandle before, each element of the feature vector
is considered as a subfeature.
The selection method
SelectionMethod is either a greedy search 'greedy'
(iteratively add the feature with highest gain)
or the dynamically oscillating search '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' is generally preferable, since it is faster.
Only in cases when the subfeatures are low-dimensional or redundant,
the method '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
GenParamValue the number
of hidden layer can be set by
'num_hidden'. The default value is '80'. Larger values
for this parameter lead to longer classification times, while it allows
a more expressive classifier.
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
select_feature_set_mlp may deliver a classifier with
a very high score. However, the classifier may perform poorly when tested.
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'
List of values: 'greedy', 'greedy_oscillating'
Names of generic parameters to configure the selection process and the classifier.
Default value: 
List of values: 'num_hidden'
→(real / integer / string)
Values of generic parameters to configure the selection process and the classifier.
Default value: 
Suggested values: 50, 80, 100
A trained MLP classifier using only the selected features.
The selected feature set, contains indices referring.
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 more data * ... * Select the better feature with a MLP select_feature_set_mlp (ClassTrainDataHandle, 'greedy', , , MLPHandle,\ SelectedFeatureMLP, Score) * Use the classifier * ...
If the parameters are valid, the operator
returns the value TRUE. If necessary, an exception is raised.