**KNIME Base Nodes** version **3.6.0.v201807061308** by **KNIME AG, Zurich, Switzerland**

Trains a Probabilistic Neural Network (PNN) based on the DDA
(Dynamic Decay Adjustment) method on labeled data using
Constructive Training of Probabilistic Neural Networks as the
underlying algorithm.

This algorithm generates rules based on numeric data. Each rule is
defined as high-dimensional Gaussian function that is adjusted by
two thresholds, theta minus and theta plus, to avoid conflicts with
rules of different classes. Each Gaussian function is defined by
a center vector (from the first covered instance) and a standard
deviation which is adjusted during training to cover only
non-conflicting instances. 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 rules after execution along with a number of of rule
measurements. The model output port contains the PNN model,
which can be used for prediction in the PNN Predictor node.

- Missing Values
- Select one method to handle missing values: "Incorp" may generate 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 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.
- PNN
**Theta Minus**This defines the upper boundary of activation for conflicting rules: default value is 0.2.

**Theta Plus**This defines the lower boundary of activation for non-conflicting rules: default value is 0.4.

- Rules as Gaussian functions, classification columns, and additional rule measures.
- PNN model can be used for prediction.

- Learner Statistics
- Displays a summary of the learning process.

- Partitioning (14) Manipulator
- File Reader (6) SourceStreamable
- CSV Reader (5) Source
- Column Filter (4) ManipulatorStreamable
- Normalizer (3) Manipulator
~~XLS Reader~~(3) SourceDeprecated- X-Partitioner (2) LoopStart
- ARFF Reader (1) Source
- Database Reader (1) Source
- Scorer (1) Other
- String To Number (1) ManipulatorStreamable
- Column Resorter (1) ManipulatorStreamable
- Correlation Filter (1) Manipulator
- Joiner (1) Manipulator
- Missing Value (1) Manipulator
- Normalizer (PMML) (1) Manipulator
- Transpose (1) Manipulator
- Color Manager (1) Visualizer
~~Excel Reader (XLS)~~(1) SourceStreamableDeprecated- Java Edit Variable (0) Manipulator
- PNN Learner (DDA) (0) Learner
- Category To Number (0) ManipulatorStreamable
- Number To String (0) Manipulator
- Linear Correlation (0) Other
- Row Splitter (0) ManipulatorStreamable
- Column Rename (0) ManipulatorStreamable
- Numeric Row Splitter (0) Manipulator
- Single sample t-test (0) Manipulator
- Shape Manager (0) Visualizer
- Date Field Extractor (legacy) (0) Manipulator
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- PNN Predictor (124) PredictorStreamable
- Model Writer (4) Sink
- X-Aggregator (1) LoopEnd
- Scorer (1) Other
- Association Rule Learner (1) Learner
- Constant Value Column (1) ManipulatorStreamable
- ROC Curve (1) Visualizer
- CSV Writer (0) SinkStreamable
- Java Edit Variable (0) Manipulator
- Naive Bayes Learner (0) Learner
- Fuzzy Rule Learner (0) Learner
- PNN Learner (DDA) (0) Learner
- Decision Tree Learner (0) Learner
~~Random Forest Learner~~(0) LearnerDeprecated- HiLite Table (0) Visualizer
- Boosting Learner Loop End (0) LoopEnd
- Rule Viewer (0) Visualizer
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To use this node in KNIME, install **KNIME Base Nodes** from the following update site: