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Disjunctive Rule Lists

Author

Listed:
  • Ronilo Ragodos

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Tong Wang

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

Abstract

In this study, we present an interpretable model, disjunctive rule list (DisRL) for regression. This research is motivated by the increasing need for model interpretability, especially in high-stakes decisions such as medicine, where decisions are made on or related to humans. DisRL is a generalized form of rule lists. A DisRL model consists of a list of disjunctive rules embedded in an if-else logic structure that stratifies the data space. Compared with traditional decision trees and other rule list models in the literature that stratify the feature space with single itemsets (an itemset is a conjunction of conditions), each disjunctive rule in DisRL uses a set of itemsets to collectively cover a subregion in the feature space. In addition, a DisRL model is constructed under a global objective that balances the predictive performance and model complexity. To train a DisRL model, we devise a hierarchical stochastic local search algorithm that exploits the properties of DisRL’s unique structure to improve search efficiency. The algorithm adopts the main structure of simulated annealing and customizes the proposing strategy for faster convergence. Meanwhile, the algorithm uses a prefix bound to locate a subset of the search area, effectively pruning the search space at each iteration. An ablation study shows the effectiveness of this strategy in pruning the search space. Experiments on public benchmark datasets demonstrate that DisRL outperforms baseline interpretable models, including decision trees and other rule-based regressors.

Suggested Citation

  • Ronilo Ragodos & Tong Wang, 2022. "Disjunctive Rule Lists," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3259-3276, November.
  • Handle: RePEc:inm:orijoc:v:34:y:2022:i:6:p:3259-3276
    DOI: 10.1287/ijoc.2022.1242
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    References listed on IDEAS

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    1. Bruce Hajek, 1988. "Cooling Schedules for Optimal Annealing," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 311-329, May.
    2. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    3. Stanislav Vojíř & Tomáš Kliegr, 2020. "Editable machine learning models? A rule-based framework for user studies of explainability," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 785-799, December.
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