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An Efficient Algorithm For Finding Optimal Gain-Ratio
Multiple-Split
Tests On Hierarchical Attributes In Decision Tree Learning
Almuallim, H; Akiba, Y; Kaneda, S
AMER ASSOC ARTIFICIAL INTELLIGENCE, PROCEEDINGS OF THE THIRTEENTH
NATIONAL CONFERENCE ON ARTIFICIAL
INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL
INTELLIGENCE CONFERENCE, VOLS 1 AND 2; pp: 703-708; Vol: ##
King Fahd University of Petroleum & Minerals
http://www.kfupm.edu.sa
Summary
Given a set of training examples S and a tree-structured attribute x, the goal in this
work is to find a multiple-split test defined on x that maximizes Quinlan's gain-ratio
measure. The number of possible such multiple-split tests grows exponentially in the
size of the hierarchy associated with the attribute. It is, therefore, impractical to
enumerate and evaluate all these tests in order to choose the best one. We introduce an
efficient algorithm for solving this problem that guarantees maximizing the gain-ratio
over all possible tests. For a training set of m examples and an attribute hierarchy of
height d, our algorithm runs in time proportional to dm, which makes it efficient
enough for practical use.
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Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa
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Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa