FLR (3.7)

Fuzzy Lattice Reasoning Classifier (FLR) v5.0 The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. The current version can be used for classification using numeric predictors. For more information see: I

N.Athanasiadis, V.

G.Kaburlasos, P.

A.Mitkas, V.

Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support.In: 1st Intl.

NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003.


Kaburlasos, I.N.

Athanasiadis, P.A.

Mitkas, V.Petridis (2003).

Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.

(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.


FLR Options

R: Set vigilance parameter rhoa. (a float in range [0,1])

B: Set boundaries File Note: The boundaries file is a simple text file containing a row with a Fuzzy Lattice defining the metric space. For example, the boundaries file could contain the following the metric space for the iris dataset: [ 4.3 7.9 ] [ 2.0 4.4 ] [ 1.0 6.9 ] [ 0.1 2.5 ] in Class: -1 This lattice just contains the min and max value in each dimension. In other kind of problems this may not be just a min-max operation, but it could contain limits defined by the problem itself. Thus, this option should be set by the user. If ommited, the metric space used contains the mins and maxs of the training split.

Y: Show Rules

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: [Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values] 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

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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.


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