Automatically assigned DDC number: 00631

Manually assigned DDC number: 00631

Title: Automatic Selection of Split Criterion during Tree Growing Based on Node Location

Author:

Subject: Carla E. Brodley Automatic Selection of Split Criterion during Tree Growing Based on Node Location

Description: Typically, decision tree construction algorithms apply a single "goodness of split" criterion to form each test node of the tree. It is a hypothesis of this research that better results can be obtained if during tree construction one applies a split criterion suited to the "location" of the test node in the tree. Specifically, given the objective of maximizing predictive accuracy, test nodes near the root of the tree should be chosen using a measure based on information theory, whereas test nodes closer to the leaves of the pruned tree should be chosen to maximize classification accuracy on the training set. The results of an empirical evaluation illustrate that adapting the split criterion to node location can improve classification performance. 1 DECISION TREE CONSTRUCTION A decision tree is either a leaf node containing a classification or an attribute test, with for each value of the attribute, a branch to a decision tree. To classify an instance using a decision tree, one starts ...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1995-08-02

Pubyear: 1995

Format: ps

Identifier: http://citeseer.ist.psu.edu/140359.html

Source: http://mow.ecn.purdue.edu/~lrn/publications/95-mlc-brodley-a.ps.gz

Language: en

Rights: unrestricted

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