Text Classifier Model Pruner

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The “Text Classifier Model Pruner” allows to reduce the size of a text classification model by applying different pruning methods. On the one hand, low frequency terms which were encountered during training can be removed. Our experience shows, that setting this value e.g. to “2” roughly reduces the number of terms in the model to half the amount without significantly harming classification quality (your mileage may vary).

On the other hand, an information-gain-based pruning strategy is available, which scores the terms and their associated category probabilities. A good explanation of the information gain method can be found in “A Comparative Study on Feature Selection in Text Categorization”, Yiming Yang and Jan O. Pedersen, 1997.


Minimum term count
The minimum count (i.e. the number of training documents in which it has to appear) to keep a term, set to one to keep all terms.
Minimum information gain
The minimum information gain to keep a term. Set to zero to keep all terms.

Input Ports

The model data of the trained classifier.

Output Ports

The pruned model, where terms not satisfying the given properties have been removed.


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