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
Popular Predecessors
- Partitioning12 %
Excel Reader (XLS)5 %Scorer4 %- Column Filter3 %
Joiner3 %- File Reader3 %
String To Number3 %Number To String2 %- CSV Reader2 %
- Excel Reader2 %
- Normalizer2 %
- Row Filter2 %
- k-Means2 %
Linear Correlation1 %- Table View (JavaScript)1 %
Numeric Scorer1 %- Correlation Filter1 %
- PNN Predictor1 %
- Parameter Optimization Loop Start1 %
- GroupBy1 %
- ROC Curve (legacy)1 %
- ARFF Reader1 %
Database Reader1 %- Bootstrap Sampling1 %
Color Appender1 %- Scorer1 %
- SMOTE< 1 %
- X-Partitioner< 1 %
- Missing Value< 1 %
Table Row To Variable Loop Start< 1 %Naive Bayes Predictor< 1 %- PNN Learner (DDA)< 1 %
- Category to Number< 1 %
- Row Splitter< 1 %
- Column Filter (PMML)< 1 %
File Reader< 1 %- Constant Value Column< 1 %
- Round Double< 1 %
- Row Sampling< 1 %
- Table Transposer< 1 %
- Rule-based Row Splitter< 1 %
- Scatter Matrix (legacy)< 1 %
- Math Formula< 1 %
- Number to String< 1 %
- Numeric Outliers< 1 %
- DB Query Reader< 1 %
- Table Reader< 1 %
Recursive Loop Start< 1 %- Column Resorter< 1 %
- Missing Value Column Filter< 1 %
- Pivot< 1 %
- Data Generator< 1 %
XLS Reader< 1 %Excel Reader (XLS)< 1 %Read Excel Sheet Names (XLS)< 1 %- Cluster Assigner< 1 %
- SVM Predictor< 1 %
CSV Reader< 1 %- MultiLayerPerceptron Predictor< 1 %
- Linear Regression Learner< 1 %
- Domain Calculator< 1 %
- Column Splitter< 1 %
- Rule Engine< 1 %
- Crosstab< 1 %
- Example Data< 1 %
- Fingerprints Expander< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Fuzzy c-Means< 1 %
- K Nearest Neighbor< 1 %
- Logistic Regression Learner< 1 %
- Double to Integer< 1 %
- String Manipulation< 1 %
- Rule-based Row Filter< 1 %
Extract Date&Time Fields< 1 %Table Row to Variable< 1 %- Table Creator< 1 %
PCA< 1 %- Normalizer (PMML)< 1 %
Column Rename< 1 %- Numeric Row Splitter< 1 %
- Single sample t-test< 1 %
- Color Manager< 1 %
- Shape Manager< 1 %
- Line Plot (JavaScript)< 1 %
JSON Reader< 1 %- Network to Row< 1 %
- R Source (Table)< 1 %
- Date Field Extractor (legacy)< 1 %
Backward Feature Elimination Start (1:1)< 1 %- Fuzzy Rule Learner< 1 %
- Hierarchical Clustering< 1 %
- Concatenate< 1 %
- Auto-Binner< 1 %
- Equal Size Sampling< 1 %
- Reference Column Filter< 1 %
- Nominal Value Row Filter< 1 %
- Parallel Chunk Start< 1 %
- Boosting Learner Loop Start< 1 %
- Column Filter (legacy)< 1 %
- String to Date&Time< 1 %
- Twitter Timeline< 1 %
- RDKit Descriptor Calculation< 1 %
- Table Row to Variable< 1 %
- Java Snippet< 1 %
Table Reader< 1 %- Java Edit Variable< 1 %
Backward Feature Elimination Start (2:2)< 1 %- X-Aggregator< 1 %
- Decision Tree Predictor< 1 %
- Numeric Binner< 1 %
- Box Plot (legacy)< 1 %
- OpenNLP NE Tagger< 1 %
- Number Formatter< 1 %
Active Learn Loop Start< 1 %- Split Collection Column< 1 %
- Strings to Binary Objects< 1 %
- Read Images< 1 %
Database Connection Table Reader< 1 %Line Reader< 1 %List Files< 1 %- Feature Selection Filter< 1 %
- Feature Selection Loop Start (2:2)< 1 %
Counting Loop Start< 1 %- Column List Loop Start< 1 %
Recursive Loop Start (2 ports)< 1 %Naive Bayes Learner< 1 %- Naive Bayes Learner< 1 %
- Naive Bayes Predictor< 1 %
- Fuzzy Rule Predictor< 1 %
- DBSCAN< 1 %
K Nearest Neighbor< 1 %PCA Apply< 1 %- Polynomial Regression Learner< 1 %
- Entropy Scorer< 1 %
- Numeric Scorer< 1 %
- SOTA Predictor< 1 %
- Association Rule Learner< 1 %
- PCA Apply< 1 %
- PCA< 1 %
- Gradient Boosted Trees Predictor< 1 %
- Tree Ensemble Predictor< 1 %
- Binner (Dictionary)< 1 %
- Create Bit Vector< 1 %
- Cell Splitter< 1 %
- Column Comparator< 1 %
- Category to Number (Apply)< 1 %
- Number to Category (Apply)< 1 %
- String to Number< 1 %
Column Appender< 1 %- Column Rename (Regex)< 1 %
- One to Many< 1 %
- Linear Correlation< 1 %
- Table Validator< 1 %
- Edit Numeric Domain< 1 %
- Nominal Value Row Splitter< 1 %
- Low Variance Filter< 1 %
- Normalizer (Apply)< 1 %
- Missing Value (Apply)< 1 %
String To Number (PMML)< 1 %- Rank< 1 %
- RowID< 1 %
- String Replacer< 1 %
String Replace (Dictionary)< 1 %- Decision Tree to Ruleset< 1 %
- Cronbach Alpha< 1 %
Rank Correlation< 1 %- Rank Correlation< 1 %
- Statistics< 1 %
- Extract Table Spec< 1 %
- Distance Matrix Reader< 1 %
- Column Expressions< 1 %
- H2O to Table< 1 %
- Math Formula (Multi Column)< 1 %
Read XLS Sheet Names< 1 %Excel Reader (XLS)< 1 %- Bag Of Words Creator< 1 %
- Decision Tree View (JavaScript)< 1 %
- Scatter Plot (JavaScript)< 1 %
- R to Table< 1 %
- Moving Average< 1 %
XML Reader< 1 %- Fingerprints< 1 %
- Chunk Loop Start< 1 %
- Simple Regression Tree Predictor< 1 %
- Expand Bit Vector< 1 %
- Joiner< 1 %
- Denormalizer (PMML)< 1 %
- String to Number (PMML)< 1 %
- String Manipulation (Multi Column)< 1 %
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
- Show all 37 workflows
Links
- No links available
Developers
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