IconVFI (3.7)0 ×

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

Classification by voting feature intervals

Intervals are constucted around each class for each attribute (basically discretization).Class counts are recorded for each interval on each attribute.

Classification is by voting.For more info see:

G.

Demiroz, A.Guvenir: Classification by voting feature intervals.

In: 9th European Conference on Machine Learning, 85-92, 1997.

Have added a simple attribute weighting scheme.Higher weight is assigned to more confident intervals, where confidence is a function of entropy:

weight (att_i) = (entropy of class distrib att_i / max uncertainty)^-bias

(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.

Options

VFI Options

C: Don't weight voting intervals by confidence

B: Set exponential bias towards confident intervals (default = 0.6)

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, Missing class values] Dependencies: [] min # Instance: 0

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

Views

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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