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.

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: Replaces the missing value(s) with the column mean.
• Min: Replaces the missing value(s) with the column min.
• Max: Replaces the missing value(s) with the column max.
• Zero: Replaces the missing value(s) with zero.
• One: Replaces the missing value(s) with 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.

Use maximum number of 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.

Theta plus

This defines the lower boundary of activation for non-conflicting rules.

Views

Learner Statistics

Displays a summary of the learning process.

Workflows

• 4. CASE SwitchKNIME Hub
• 4. CASE SwitchKNIME Hub
• 4. CASE SwitchKNIME Hub
• 4. CASE SwitchKNIME Hub
• 4. CASE SwitchKNIME Hub
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