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Decision trees offer the benefit of easy interpretation because they allowthe classification of input data based on if--then rules. However, as decisiontrees are constructed by an algorithm that achieves clear classification withminimum necessary rules, the trees possess the drawback of extracting onlyminimum rules, even when various latent rules exist in data. Approaches thatconstruct multiple trees using randomly selected feature subsets do exist.However, the number of trees that can be constructed remains at the same scalebecause the number of feature subsets is a combinatorial explosion.Additionally, when multiple trees are constructed, numerous rules aregenerated, of which several are untrustworthy and/or highly similar. Therefore,we propose "MAABO-MT" and "GS-MRM" algorithms that strategically constructtrees with high estimation performance among all possible trees with smallcomputational complexity and extract only reliable and non-similar rules,respectively. Experiments are conducted using several open datasets to analyzethe effectiveness of the proposed method. The results confirm that MAABO-MT candiscover reliable rules at a lower computational cost than other methods thatrely on randomness. Furthermore, the proposed method is confirmed to providedeeper insights than single decision trees commonly used in previous studies.Therefore, MAABO-MT and GS-MRM can efficiently extract rules fromcombinatorially exploded decision trees.

Multi-rules mining algorithm for combinatorially exploded decision trees with modified Aitchison-Aitken function-based Bayesian optimization