IconOneClassClassifier (3.7)0 ×

KNIME WEKA nodes (3.7) version 3.6.0.v201805031010 by KNIME AG, Zurich, Switzerland

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes

The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class.

Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier.If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class.

The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset.It cansimply be used as a flag, you do not need to relabel any classes.

For more information, see:

Kathryn Hempstalk, Eibe Frank, Ian H.

Witten: One-Class Classification by Combining Density and Class Probability Estimation.In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


OneClassClassifier Options

trr: Sets the target rejection rate (default: 0.1)

tcl: Sets the target class label (default: 'target')

cvr: Sets the number of times to repeat cross validation to find the threshold (default: 10)

P: Sets the proportion of generated data (default: 0.5)

cvf: Sets the percentage of heldout data for each cross validation fold (default: 10)

num: Sets the numeric generator (default: weka.classifiers.meta.generators.GaussianGenerator)

nom: Sets the nominal generator (default: weka.classifiers.meta.generators.NominalGenerator)

L: Sets whether to correct the number of classes to two, if omitted no correction will be made.

E: Sets whether to exclusively use the density estimate.

I: Sets whether to use instance weights.

S: Random number seed. (default 1)

D: If set, classifier is run in debug mode and may output additional info to the console

W: Full name of base classifier. (default: weka.classifiers.meta.Bagging)

P: Size of each bag, as a percentage of the training set size. (default 100)

O: Calculate the out of bag error.

S: Random number seed. (default 1)

num-slots: Number of execution slots. (default 1 - i.e. no parallelism)

I: Number of iterations. (default 10)

D: If set, classifier is run in debug mode and may output additional info to the console

W: Full name of base classifier. (default: weka.classifiers.trees.REPTree)

M: Set minimum number of instances per leaf (default 2).

V: Set minimum numeric class variance proportion of train variance for split (default 1e-3).

N: Number of folds for reduced error pruning (default 3).

S: Seed for random data shuffling (default 1).

P: No pruning.

L: Maximum tree depth (default -1, no maximum)

I: Initial class value count (default 0)

R: Spread initial count over all class values (i.e. don't use 1 per value)

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Unary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model


Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Update Site

To use this node in KNIME, install KNIME WEKA nodes (3.7) from the following update site:

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