Multi-rules mining algorithm for combinatorially exploded decision trees
  with modified Aitchison-Aitken function-based Bayesian optimization
Yuto Omae Masaya Mori Yohei Kakimoto
Decision trees offer the benefit of easy interpretation because they allow
the classification of input data based on if--then rules. However, as decision
trees are constructed by an algorithm that achieves clear classification with
minimum necessary rules, the trees possess the drawback of extracting only
minimum rules, even when various latent rules exist in data. Approaches that
construct multiple trees using randomly selected feature subsets do exist.
However, the number of trees that can be constructed remains at the same scale
because the number of feature subsets is a combinatorial explosion.
Additionally, when multiple trees are constructed, numerous rules are
generated, of which several are untrustworthy and/or highly similar. Therefore,
we propose "MAABO-MT" and "GS-MRM" algorithms that strategically construct
trees with high estimation performance among all possible trees with small
computational complexity and extract only reliable and non-similar rules,
respectively. Experiments are conducted using several open datasets to analyze
the effectiveness of the proposed method. The results confirm that MAABO-MT can
discover reliable rules at a lower computational cost than other methods that
rely on randomness. Furthermore, the proposed method is confirmed to provide
deeper insights than single decision trees commonly used in previous studies.
Therefore, MAABO-MT and GS-MRM can efficiently extract rules from
combinatorially exploded decision trees.