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Creating an ANN Classifier

 

Overview

In GeneLinkerô, an ANN Classifier is actually a committee of artificial neural networks (ANNs).

Note: The terms Learner, Component Classifier, and Artificial Neural Network (ANN) are interchangeable. The term Classifier refers to an ensemble (committee) of learners.

 

Classifier Parameter Descriptions

Learners

Learner Votes Required

Hidden Units

Conjugate Gradient Method

Steps

Stopping Criteria: MSE Fractional Change

Stopping Criteria: Maximum Iterations

Random Seed

 

Actions

1. Click a dataset that has variable information associated with it in the Experiments navigator. The item is highlighted.

2. Click the Create ANN Classifier toolbar icon , or select Create ANN Classifier from the Predict menu, or right-click the item and select Create ANN Classifier from the shortcut menu. The Create ANN Classifier parameters dialog is displayed.

3. Set the parameters.

Parameter

Description

Representative Variable

A list of all the valid variables associated with this dataset are shown in the listbox. Select the variable that specifies the correct class values that the classifier is to be trained to predict. A "valid variable" is one that has all values for the variable represented in the dataset. For various reasons it is difficult to properly handle all cases where only a few of the possible values for a variable are represented in the training data.

Learners

The number of component learners in the classifier.

Learner Votes Required

The threshold at which the classifier will make a prediction.

Hidden Units

The number of nodes in the hidden layer of the learner.

Conjugate Gradient Method

Specifies the variant of the method to use.

Steps

The number of conjugate gradient steps between evaluations of the stopping criteria.

MSE Fractional Change

Learner training stops when the MSE drops less than this threshold between two successive iterations.

Maximum Iterations

The maximum number of times to evaluate the MSE for a learner.

Random Seed

Seed value for the random number generator.

 

4. Click OK. The Experiment Progress dialog is displayed. It is dynamically updated as the Create Classifier operation is performed. To cancel the Create Classifier operation, click the Cancel button.

Upon successful completion, a new item (Trained Classifier) is added under the original item in the Experiments navigator.

 

Related Topics:

ANN Classification and Prediction Overview

Classify New Data

Classification Plot

MSE Plot