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Classifiers

Some algorithms to develop classification models of data. This library uses dataset-utils; another classification algorithm is provided in libsvm.

Exported Procedures

The classifiers are constructed using a (build-X training-data target-class) procedure. Training data are a relation, as defined in dataset-utils, and target-class is the name of the attribute to be used for the target classification.

Generic procedures

[procedure] (classify-instance classifier instance)

Given a classifier and a data instance, returns a classification.

[procedure] (to-string classifier)

Given a classifier, returns a string representation of the classifier model.

ZeroR

A simple rule: always predicts the majority class of the training data.

[procedure] (build-zero-r training-data target-class)

Constructs an instance of a ZeroR classifier. Training data are a relation, as defined in dataset-utils, and target-class is the name of the attribute to be used for the target classification.

OneR

Finds the attribute which best predicts the training data.

[procedure] (build-one-r training-data target-class)

Constructs an instance of a OneR classifier. Training data are a relation, as defined in dataset-utils, and target-class is the name of the attribute to be used for the target classification.

ID3

[procedure] (build-id3 training-data target-class)

Constructs an instance of a decision-tree classifier using the ID3 algorithm. Training data are a relation, as defined in dataset-utils, and target-class is the name of the attribute to be used for the target classification. All attributes must be nominal.

Author

Peter Lane.

License

GPL version 3.0.

Requirements

Works with dataset-utils.

Version History

in trunk.