Learning Small Decision Trees for Data of Low Rank-Width

Konrad K. Dabrowski, Eduard Eiben, Sebastian Ordyniak, Giacomo Paesani, Stefan Szeider

Abstract:

We consider the NP-hard problem of finding a smallest decision tree representing a classification instance in terms of a partially defined Boolean function. Small decision trees are desirable to provide an interpretable model for the given data. We show that the problem is fixed-parameter tractable when parameterized by the rank-width of the incidence graph of the given classification instance. Our algorithm proceeds by dynamic programming using an NLC decomposition obtained from a rank-width decomposition. The key to the algorithm is a
succinct representation of partial solutions. This allows us to limit the space and time requirements for each dynamic programming step in
terms of the parameter.