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FLAIRS
2006
13 years 6 months ago
Generalized Entropy for Splitting on Numerical Attributes in Decision Trees
Decision Trees are well known for their training efficiency and their interpretable knowledge representation. They apply a greedy search and a divide-and-conquer approach to learn...
Mingyu Zhong, Michael Georgiopoulos, Georgios C. A...
IJCAI
1993
13 years 6 months ago
Induction of Oblique Decision Trees
This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to nd a good ob...
David G. Heath, Simon Kasif, Steven Salzberg
ICANNGA
2007
Springer
100views Algorithms» more  ICANNGA 2007»
13 years 8 months ago
Softening Splits in Decision Trees Using Simulated Annealing
Predictions computed by a classification tree are usually constant on axis-parallel hyperrectangles corresponding to the leaves and have strict jumps on their boundaries. The densi...
Jakub Dvorák, Petr Savický
COR
2006
97views more  COR 2006»
13 years 4 months ago
Evaluating the performance of cost-based discretization versus entropy- and error-based discretization
Discretization is defined as the process that divides continuous numeric values into intervals of discrete categorical values. In this article, the concept of cost-based discretiz...
Davy Janssens, Tom Brijs, Koen Vanhoof, Geert Wets
DATAMINE
1999
143views more  DATAMINE 1999»
13 years 4 months ago
Partitioning Nominal Attributes in Decision Trees
To find the optimal branching of a nominal attribute at a node in an L-ary decision tree, one is often forced to search over all possible L-ary partitions for the one that yields t...
Don Coppersmith, Se June Hong, Jonathan R. M. Hosk...