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GMLVQClassifier

Trains a classifier based on the given sample data, or predicts with a classifier trained earlier.

MATLAB code: Kerstin Bunte (modified based on the code of Marc Strickert http://www.mloss.org/software/view/323/ and Petra Schneider). uses the Fast Limited Memory Optimizer fminlbfgs.m written by Dirk-Jan Kroon available at the MATLAB central. kerstin.bunte@googlemail.com

Classifier methods:

Version 1.1
Bundle tools
Categories Classification
Authors Ville Rantanen (ville.rantanen@helsinki.fi)
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Requires Matlab
Source files component.xml gmlvq_confusion.m gmlvq_evaluation.m gmlvq_predict_start.m gmlvq_train_start.m gmlvq_preprocess.m gmlvq_call.m
Usage Example with default values

Inputs

Name Type Mandatory Description
data CSV Optional Sample data for the supervised learning.
testData CSV Optional Validation data to estimate accuracy of the new classifier. If not given, input data is used.
classifyData CSV Optional Data for which classes are predicted. NOTE This is not used in training or in validation! Weka requires class-column also for this dataset. You should add a column named with the parameter 'classColumn' to this dataset. It is a good trick to name id-column as 'classColumn', in this case it is also added to the 'predictedClasses' data set.
inClassifier MatlabBinary Optional A classifier object that is used instead of building new classifier based on training data. NOTE If this is set parameter 'method' or input 'data' are not used, you should still provide these values (empty values).

Outputs

Name Type Description
outClassifier MatlabBinary A new classifier that has been produced.
confusion Matrix Confusion matrix with the class prediction frequencies as columns
importances CSV Importances of the features when training. Cannot be produced when not in training mode.
evaluation CSV Evaluation
predictedClasses CSV If input 'classifydata' is provided, classes are predicted for the data and results are in this output. Otherwise this output is an empty file.

Parameters

Name Type Default Description
classColumn string "" Column name for the column that contains the reference class.
columnsToRemove string "" Comma separated list of names of columns not to be used in classification. Useful if you want to ignore some attribute in the data while teaching the classifier.
iterations int 1 Iterate training, return the one that creates minimum validation data error.
method string "GMLVQ" Choose from GMLVQ, LGMLVQ, GRLVQ.
parameters string "" A space separated list of parameters passed to clustering method.
prototypes int 1 Prototypes per class

Test cases

Test case Parameters IN
data
IN
testData
IN
classifyData
IN
inClassifier
OUT
outClassifier
OUT
confusion
OUT
importances
OUT
evaluation
OUT
predictedClasses
case1_train properties data (missing) (missing) (missing) (missing) confusion importances evaluation predictedClasses

classColumn = Diagnosis,
method = LGMLVQ,
iterations = 2

case2_classifying properties (missing) (missing) classifyData inClassifier (missing) confusion (missing) evaluation predictedClasses

columnsToRemove = File,Diagnosis,
method = LGMLVQ

case3_classify_with_known properties (missing) (missing) classifyData inClassifier (missing) confusion (missing) evaluation predictedClasses

classColumn = Diagnosis,
columnsToRemove = File,Diagnosis,
method = LGMLVQ

case4_train_and_predict properties data (missing) classifyData (missing) (missing) confusion importances evaluation predictedClasses

classColumn = Diagnosis,
columnsToRemove = File,
method = GMLVQ,
prototypes = 2,
iterations = 5


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