28916@AAAI

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#1 Learning Small Decision Trees for Data of Low Rank-Width [PDF4] [Copy] [Kimi8] [REL]

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

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.

Subject: AAAI.2024 - Knowledge Representation and Reasoning