Wrap the selected KNN and use its feedback to guide the search for the optimal feature weights. A FeatureWeights object will be created.

Settings

Learning rate

The rate at which the maximum distance between the pivot and a random seed is decremented.

Number of seeds

The size of the feature weight neighbourhood.

Stop at

A value specifying a stopping criterion. When feature weights yielding accuracy estimates higher than the specified value the search will stop. A value of 1 imposes no constraints whereas a value of 0.5 will result in the termination of the search algorithm once feature weights resulting in an classification accuracy of 50 percent or better are found.

Optimization

Specifies whether to search for all features simultaneously or one at a time.

Evaluation method

The method to be used for estimating the classification accuracy. Supported methods are 10-fold cross-validation and leave-one-out.

k neighbours

The size of the neighbourhood used for feedback classification.

Vote weighting

The type of vote weighting to be used.

See also:

kNN classifiers 1.1.1.2. Wrapper-based feature weighting

kNN classifiers 1.1.1. Feature weighting

kNN classifiers 1.1. Improving classification accuracy

KNN & Pattern & Categories & FeatureWeights: Evaluate...

kNN classifiers 1. What is a kNN classifier?

kNN classifiers

Links to this page

• kNN classifiers 3. Command overview