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Published in:
2020 | OriginalPaper | Chapter
11. Inducing Modular Rules for Classification
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Abstract
This chapter begins by considering a method of post-pruning decision rules generated via a decision tree, which has the property that the pruned rules will not generally fit together to form a tree. Rules of this kind are known as modular rules. When using modular rules to classify unseen test data a conflict resolution strategy is needed and several possibilities for this are discussed. The use of a decision tree as an intermediate representation for rules is identified as a source of overfitting.
The Prism algorithm induces modular classification rules directly from a training set. Prism is described in detail, followed by a discussion of its performance as a classification algorithm relative to TDIDT.