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Fuzzy Rule Learner
Learns a Fuzzy Rule Model on labeled numeric data using
Mixed Fuzzy Rule Formation as the underlying training algorithm
(also known as RecBF-DDA algorithm),
see
Influence of fuzzy norms and other heuristics on
"Mixed Fuzzy Rule Formation" for an extension of the algorithm.
This algorithm generates rules based on numeric data, which are
fuzzy intervals in higher dimensional spaces. These
hyper-rectangles are defined by trapezoid fuzzy membership functions for
each dimension. The selected numeric columns of the input data are used
as input data for training and additional columns are used as
classification target, either one column holding the class information
or a number of numeric columns with class degrees between 0 and 1 can
be selected. The data output contains the fuzzy rules after execution.
Each rule consists of one fuzzy interval for each dimension plus
the target classification columns along with a number of rule
measurements. The model output port contains the fuzzy
rule model, which can be used for prediction in the Fuzzy Rule Predictor
node.
Dialog Options
- Missing Values
-
Select one method to handle missing values: "Incorp" may generate fuzzy
rules with missing values, if no replacement value has been
found during the learning process. "Best Guess" computes the optimal
replacement value by projecting the fuzzy rule (with missing value(s))
onto the missing dimension(s) of all other rules. "Mean", "Min", and "Max"
replaces the missing value with each column's statistical property.
"Zero" and "One" perform a constant replacement by inserting either zero
or one.
- Advanced
-
Shrink after commit If selected, a shrink to reduce conflicting
rules is executed immediately after a new rule is committed, i.e. the
new rule is reduced so that conflicts with all other rules of different
classes are avoided.
Use class with max coverage If selected, only the class with
maximum coverage degree of the target columns is used during training,
otherwise all class columns are considered for coverage.
- Maximum no. Epochs
-
If selected, the option defines the maximum number of epochs
the algorithm has to process the entire data set, otherwise it repeats
this process until this rule model is stable, i.e. no new rule has been
committed and/or no shrink is executed.
- Target Columns
-
Select the target(s) to be used for classification. If more
than one column (only numeric) is selected, the columns must contain
class membership values between 0 and 1 for each class given by the
column name.
- Fuzzy
-
Fuzzy Norm Select a fuzzy norm to compute the rules' activation
across all dimensions and rules. Fuzzy norms are important, because they
combine the membership values of each fuzzy interval for one rule and
compute a final output across all rules. Different choices of fuzzy
norms are available: Min/Max Norm, Product Norm, Lukasiewicz Norm, and
Yager[2.0] Norm.
Shrink Function Select a shrink method to reduce rules in order
to avoid conflicts between rules of different classes. There are three
shrink methods available to normalize the loss in volume:
VolumnBorderBased shrink applies the volume loss in terms of the support
or core region borders; VolumnAnchorBased shrink uses the anchor value
border; and VolumnRuleBased shrink uses the entire rule volume.
Ports
Input Ports
0 |
Numeric data as well as class information used for training.
|
Output Ports
0 |
Rules with fuzzy intervals in each dimension, classification columns,
and additional rule measures.
|
1 |
Fuzzy Rule Model can be used for prediction.
|
Views
- Learner Statistics
-
Displays a summary of the learning process.
This node is contained in KNIME Base Nodes
provided by KNIME GmbH, Konstanz, Germany.