LAC (3.7)

Implements the LAC (Lazy Associative Classifier) algorithm, which uses associative rules to execute classifications

Unlike other Apriori-based classifiers, LAC algorithm computes association rules in a demand-driven basis.For each instance to be classified, it filters the training set and produces only useful rules for that instance, outperforming traditional associative classifiers in both time and accuracy.

For more information: [Adriano Veloso, Wagner Meira Jr., Mohammed Zaki.Lazy Associative Classification.

ICDM '06 Proceedings of the Sixth International Conference on Data Mining, Pages 645-654, IEEE Computer Society Washington, DC, USA].

(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

LAC Options

M: Determines the maximum length of a classification rule (its number of features plus 1, because class attribute is also considered). Mining large rules is costly, but sometimes the accuracy gain is worth. The default value for this option is 4.

S: Determines the support threshold for pruning (default: 0).

C: Determines the confidence threshold for pruning (default: 0).

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, String attributes, Missing values, Nominal class, Binary class] Dependencies: [] 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

Popular Predecessors

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Popular Successors

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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.

Workflows

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Developers

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Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

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

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.0.v202312081535

On NodePit since: 2024-07-09

Last update: 2024-11-14

KNIME versions: Since v3.6

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