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ICML
2005
IEEE

Generalized skewing for functions with continuous and nominal attributes

10 years 3 months ago
Generalized skewing for functions with continuous and nominal attributes
This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary variables. In this paper, we extend skewing to directly handle functions of continuous and nominal variables. We present experiments with randomly generated functions and a number of real world datasets to evaluate the algorithm's accuracy. Our results indicate that our algorithm almost always outperforms an Information Gain-based decision tree learner.
Soumya Ray, David Page
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Soumya Ray, David Page
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