Supervised Classification

With supervised classification, you use the CTX_CLS.TRAIN procedure to automate the rule-writing step. CTX_CLS.TRAIN uses a training set of sample documents to deduce classification rules. This training set is the major advantage over rule-based classification, where you must write the classification rules.

However, before you can run the CTX_CLS.TRAIN procedure, you must manually create categories and assign each document in the sample training set to a category.

See Also: Oracle Text Reference for more information on CTX_CLS.TRAIN

When the rules are generated, you index them to create a CTXRULE index. You can then use the MATCHES operator to classify an incoming stream of new documents.

You can select one of the following classification algorithms for supervised classification:

• Decision Tree Supervised Classification

  The advantage of this classification is that the generated rules are easily observed (and modified).

• SVM-Based Supervised Classification

  This classification uses the Support Vector Machine (SVM) algorithm for creating rules. The advantage of this classification is that it is often more accurate than the Decision Tree classification. The disadvantage is that it generates binary rules, so the rules themselves are opaque.

See Also:

• “Decision Tree Supervised Classification Example”

• “SVM-Based Supervised Classification Example”