A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS(2004)
摘要
We introduce a numerical measure on sets of partitions of finite sets that is linked to the Goodman-Kruskal association index commonly used in statistics. This measure allows us to define a metric on such partions used for constructing decision trees. Experimental results suggest that by replacing the usual splitting criterion used in C4.5 by a metric criterion based on the Goodman-Kruskal coefficient it is possible, in most cases, to obtain smaller decision trees without sacrificing accuracy.
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关键词
Goodman-Kruskal association index,metric,partition,decision tree
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