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MonDT - Decision Trees for Classification with Monotonicity Constraints

This repository implements two different decision trees for monotonic classification:

  • Rank Discrimination Measure Tree (RDMT). This decision tree can be built with three different rank discrimination measures: Gini, Shannon, or Pessimistic.

    Marsala, C., & Petturiti, D. (2015). Rank discrimination measures for enforcing monotonicity in decision tree induction. Information Sciences, 291, 143-171.

  • Rank Entropy-Based Decision Trees for Monotonic Classification (REMT)

    Hu, Q., Che, X., Zhang, L., Zhang, D., Guo, M., & Yu, D. (2011). Rank entropy-based decision trees for monotonic classification. IEEE Transactions on Knowledge and Data Engineering, 24(11), 2052-2064.

Example

The file exampleMonDT is an example of the execution of these decision trees with the the Artiset data-set. The results obtained are the followings:

Algorithm Accuracy MAE NMI
RDMT_Gini 0.81 0.19 0.0
RDMT_Shannon 0.86 0.14 0.0
RDMT_Pessimistic 0.88 0.12 0.0
REMT 0.86 0.14 0.0