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Tertius (3.6)

KNIME WEKA nodes version 2.10.2.v202012020943 by KNIME AG, Zurich, Switzerland

Finds rules according to confirmation measure (Tertius-type algorithm). For more information see: P. A. Flach, N. Lachiche (1999). Confirmation-Guided Discovery of first-order rules with Tertius. Machine Learning. 42:61-95.

(based on WEKA 3.6)

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

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

Options

Class 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, Missing values, No class, Nominal class, Binary class, Missing class values] Dependencies: [] min # Instance: 1

Associator Options

K: Set maximum number of confirmation values in the result. (default: 10)

F: Set frequency threshold for pruning. (default: 0)

C: Set confirmation threshold. (default: 0)

N: Set noise threshold : maximum frequency of counter-examples. 0 gives only satisfied rules. (default: 1)

R: Allow attributes to be repeated in a same rule.

L: Set maximum number of literals in a rule. (default: 4)

G: Set the negations in the rule. (default: 0)

S: Consider only classification rules.

c: Set index of class attribute. (default: last).

H: Consider only horn clauses.

E: Keep equivalent rules.

M: Keep same clauses.

T: Keep subsumed rules.

I: Set the way to handle missing values. (default: 0)

O: Use ROC analysis.

p: Set the file containing the parts of the individual for individual-based learning.

P: Set output of current values. (default: 0)

Input Ports

Icon
Training data

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.

Installation

To use this node in KNIME, install KNIME Weka Data Mining Integration (3.6) from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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Developers

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