To use Decision Tree classification, you set the preference argument of CTX_CLS.TRAIN to RULE_CLASSIFIER.
This form of classification uses a decision tree algorithm for creating rules. Generally speaking, a decision tree is a method of deciding between two (or more, but usually two) choices. In document classification, the choices are "the document matches the training set" or "the document does not match the training set."
A decision tree has a set of attributes that can be tested. In this case, these include:
words from the document
stems of words from the document (as an example, the stem of running is run)
themes from the document (if themes are supported for the language in use)
The learning algorithm in Oracle Text builds one or more decision trees for each category provided in the training set. These decision trees are then coded into queries suitable for use by a CTXRULE index. As a trivial example, if one category is provided with a training document that consists of "Japanese beetle" and another category with a document reading "Japanese currency," the algorithm may create decision trees based on the words "Japanese," "beetle," and "currency," and classify documents accordingly.
The decision trees include the concept of confidence. Each rule that is generated is allocated a percentage value that represents the accuracy of the rule, given the current training set. In trivial examples, this accuracy is almost always 100%, but this merely represents the limitations of the training set. Similarly, the rules generated from a trivial training set may seem to be less than what you might expect, but these are sufficient to distinguish the different categories given the current training set.
The advantage of the Decision Tree method is that it can generate rules that are easily inspected and modified by a human. Using Decision Tree classification makes sense when you want to the computer to generate the bulk of the rules, but you want to fine tune them afterward by editing the rule sets.