Classification Trees With Unbiased Multiway Splits

Classification Trees With Unbiased Multiway Splits

Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the num...

Selecting Multiway Splits in Decision Trees

Decision trees in which numeric attributes are split several ways are more comprehensible than the usual binary trees because attributes rarely appear more than once in any path from root to leaf. There are efficient algorithms for finding the optimal multiway split for a numeric attribute, given the number of intervals in which it is to be divided. The problem we tackle is how to choose this n...

Selecting the Best Splits for Classification Trees With Categorical Variables

Approximating Decision Trees with Multiway Branches

We consider the problem of constructing decision trees for entity identification from a given table. The input is a table containing information about a set of entities over a fixed set of attributes. The goal is to construct a decision tree that identifies each entity unambiguously by testing the attribute values such that the average number of tests is minimized. The previously best known app...

Multiway Iceberg Cubing on Trees

The Star-cubing algorithm performs multiway aggregation on trees but incurs huge memory consumption. We propose a new algorithm MG-cubing that achieves maximal multiway aggregation. Our experiments show that MG-cubing achieves similar and very often better time and memory efficiency than Star-cubing.