Post-pruning in decision tree induction using multiple performance measures [An article from: Computers and Operations Research] | ![Post-pruning in decision tree induction using multiple performance measures [An article from: Computers and Operations Research]](http://ecx.images-amazon.com/images/I/41SS7GZHX6L._SL160_.jpg)
 enlarge | Author: K.-m. Osei-bryson
Publisher: Elsevier
Category: Book
Buy New: $7.95
Format: Html
Media: Digital
Publication Date: August 1, 2007
Availability: Available for download now
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Product Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The decision tree (DT) induction process has two major phases: the growth phase and the pruning phase. The pruning phase aims to generalize the DT that was generated in the growth phase by generating a sub-tree that avoids over-fitting to the training data. Most post-pruning methods essentially address post-pruning as if it were a single objective problem (i.e. maximize validation accuracy), and address the issue of simplicity (in terms of the number of leaves) only in the case of a tie. However, it is well known that apart from accuracy there are other performance measures (e.g. stability, simplicity, interpretability) that are important for evaluating DT quality. In this paper, we propose that multi-objective evaluation be done during the post-pruning phase in order to select the best sub-tree, and propose a procedure for obtaining the optimal sub-tree based on user provided preference and value function information.
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