PNN Learner (DDA)

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.
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.
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.

Input Ports

Numeric data as well as class information used for training.

Output Ports

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.




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