Conjecturing-Based Discovery of Patterns in Data

Published Online:https://doi.org/10.1287/ijds.2021.0043

References

  • Abolafia D, Norouzi M, Shen J, Zhao R, Le Q (2018) Neural program synthesis with priority queue training. Preprint, submitted January 10, https://arxiv.org/abs/1801.03526.Google Scholar
  • Aghaei S, Gómez A, Vayanos P (2021) Strong optimal classification trees. Preprint, submitted March 29, https://arxiv.org/abs/2103.15965.Google Scholar
  • Bellomarini L, Benedetto D, Gottlob G, Sallinger E (2020) Vadalog: A modern architecture for automated reasoning with large knowledge graphs. Inform. Systems 105:101528.Google Scholar
  • Bertsimas D, Dunn J (2017) Optimal classification trees. Machine Learning 106:1039–1082.Google Scholar
  • Blanquero R, Carrizosa E, Molero-Río C, Romero Morales D (2021) Optimal randomized classification trees. Comput. Oper. Res. 132:105281.Google Scholar
  • Bradford A, Day J, Hutchinson L, Larson CE, Mills M, Muncy D, Kaperick B, Van Cleemput N (2020) Automated conjecturing II: Chomp and intelligent game play. J. Artificial Intelligence Res. 68:447–461.Google Scholar
  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees (Routledge, New York).Google Scholar
  • Brunton S, Proctor J, Kutz J (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. USA 113:3932–3937.Google Scholar
  • Chattopadhyay I, Lipson H (2014) Data smashing: Uncovering lurking order in data. J. Royal Soc. Interface 11:20140826.Google Scholar
  • Chvátal V (1972) On Hamilton’s ideals. J. Combin. Theory Ser. B 12:163–168.Google Scholar
  • Chvátal V, Erdös P (1972) A note on Hamiltonian circuits. Discrete Math. 2(2):111–113.Google Scholar
  • Dash S, Günlük O, Wei D (2018) Boolean decision rules via column generation. 32nd Conf. Neural Inform. Processing Systems (NeurIPS-18) (Curran Associates, Red Hook, NY), 4660–4670.Google Scholar
  • Elton D (2020) Self-explaining AI as an alternative to interpretable AI. Preprint, submitted February 12, https://arxiv.org/abs/2002.05149.Google Scholar
  • Fajtlowicz S (1995) On conjectures of graffiti. Graph Theory, Combinatorics, and Algorithms, vol. 1 (Wiley, New York), 367–376.Google Scholar
  • Fürnkranz J, Kliegr T, Paulheim H (2020) On cognitive preferences and the plausibility of rule-based models. Machine Learning 109:853–898.Google Scholar
  • Haemers W (1979) On some problems of Lovász concerning the shannon capacity of a graph. IEEE Trans. Inform. Theory 25(2):231–232.Google Scholar
  • Hammer P, Bonates T (2006) Logical analysis of data—An overview: From combinatorial optimization to medical applications. Ann. Oper. Res. 148: 203–225.Google Scholar
  • Hu D, Li J, Gao R, Wang S, Li Q, Chen S, Huang J, et al. (2021) Decreased CO2 levels as indicators of possible mechanical ventilation-induced hyperventilation in COVID-19 patients: A retrospective analysis. Frontiers Public Health 8:596168.Google Scholar
  • Jantzen B (2016) Dynamical kinds and their discovery. Preprint, submitted December 15. https://arxiv.org/abs/1612.04933.Google Scholar
  • Kanter JM, Veeramachaneni K (2015) Deep feature synthesis: Toward automating data science endeavors. 2015 IEEE Internat. Conf. Data Sci. Advanced Analytics (Institute of Electrical and Electronics Engineers, Piscataway, NJ).Google Scholar
  • Katz G, Shin ECR, Song D (2016) ExploreKit: Automatic feature generation and selection. 16th IEEE Internat. Conf. Data Mining (Institute of Electrical and Electronics Engineers, Piscataway, NJ).Google Scholar
  • Khurana U, Samulowitz H, Turaga D (2018) Feature engineering for predictive modeling using reinforcement learning. 32nd AAAI Conf. Artificial Intelligence (AAAI-18) (Association for the Advancement of Artificial Intelligence, Palo Alto, CA).Google Scholar
  • Langely P, Simon HA, Bradshaw GL, Zytkow JM (1987) Scientific Discovery: Computational Explorations of the Creative Process (MIT Press, Cambridge, MA).Google Scholar
  • Langley P (2019) Scientific discovery, causal explanation, and process model induction. Mind Soc. 18:43–56.Google Scholar
  • Larson CE, Van Cleemput N (2016) Automated conjecturing I: Fajtlowicz’s Dalmatian heuristic revisited. Artificial Intelligence 231:17–38.Google Scholar
  • Larson CE, Van Cleemput N (2017) Automated conjecturing III: Property-relations conjectures. Ann. Math. Artificial Intelligence 81(3):315–327.Google Scholar
  • Lemadjeng AC, Rober T, Akyuz MH, Birbil SI (2023) Rule generation for classification: Scalability, interpretability, and fairness. Preprint, submitted August 30, https://arxiv.org/abs/2104.10751v3.Google Scholar
  • Lovász L (1979) On the Shannon capacity of a graph. IEEE Transactions Information Theory 25(1):1–7.Google Scholar
  • Lu J, Lee DK, Kim T, Danks D (2019) Good explanation for algorithmic transparency. Preprint, submitted November 11, https://dx.doi.org/10.2139/ssrn.3503603.Google Scholar
  • Nguyen Q, Nguyen X, O’Neill M, McKay R, Galván-López E (2011) Semantically-based crossover in genetic programming: Application to real-valued symbolic regression. Genetic Programming Evolvable Machines 12:91–119.Google Scholar
  • Nicolau M, Agapitos A (2021) Choosing function sets with better generalisation performance for symbolic regression models. Genetic Programming Evolvable Machines 22:73–100.Google Scholar
  • Noori M, Nejadghaderi S, Sullman M, Carson-Chahhoud K, Kolahi AA, Safiri S (2022) Epidemiology, prognosis and management of potassium disorders in Covid-19. Rev. Medical Virology 32:e2262.Google Scholar
  • Petersen B, Larma M, Mundhenk T, Santiago C, Kim S, Kim J (2021) Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. Proc. Internat. Conf. Learning Representation (ICLR) (International Conference on Learning Representations, Appleton, WI).Google Scholar
  • Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1:206–215.Google Scholar
  • Rudin C, Ertekin S (2018) Learning customized and optimized lists of rules with mathematical programming. Math. Programming Comput. 10:659–702.Google Scholar
  • Samek W, Müller KR (2019) Toward explainable artificial intelligence. Samek W, Montavon G, Vedaldi A, Hanson L, Müller KR, eds. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Springer Nature, Cham, Switzerland), 5–22.Google Scholar
  • Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(5923):81–85.Google Scholar
  • Schrijver A (2003) Combinatorial Optimization: Polyhedra and Efficiency, vol. 24 (Springer-Verlag, Berlin, Heidelberg, Germany).Google Scholar
  • Tallorin L, Wang JL, Kim WE, Sahu S, Kosa NM, Yang P, Thompson M, et al. (2018) Discovering de novo peptide substrates for enzymes using machine learning. Nature Comm. 9(1):1–10.Google Scholar
  • Therneau T, Atkinson B (2019) rpart: Recursive partitioning and regression trees. R package version 4.1-15. Retrieved May 19, 2021, https://CRAN.R-project.org/package=rpart.Google Scholar
  • Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J. Royal Statist. Soc. B 58:267–288.Google Scholar
  • Tsang M, Cheng D, Liu Y (2018a) Detecting statistical interactions from neural network weights. Sixth Internat. Conf. Learn. Representations (ICLR-18) (International Conference on Learning Representations, Appleton, WI).Google Scholar
  • Tsang M, Rambhatla H, Liu Y (2020) How does this interaction affect me? Interpretable attribution for feature interactions. 34th Conf. Neural Inform. Processing Systems (NeurIPS-20) (Curran Associates, Red Hook, NY), 6147–6159.Google Scholar
  • Tsang M, Liu H, Purushotham S, Pavankumar M, Liu Y (2018b) Neural interaction transparency (NIT): Disentangling learned interactions for improved interpretability. 32nd Conf. Neural Inform. Processing Systems (NeurIPS-18) (Curran Associates, Red Hook, NY), 5809–5818.Google Scholar
  • Udrescu SM, Tegmark M (2020) AI Feynman: A physics-inspired method for symbolic regression. Sci. Adv. 6:eaay2631.Google Scholar
  • Verwer S, Zhang Y (2019) Learning optimal classification trees using a binary linear program formulation. 33rd AAAI Conf. Artificial Intelligence (AAAI-19) (Association for the Advancement of Artificial Intelligence, Palo Alto, CA).Google Scholar
  • Vilone G, Longo L (2020) Explainable artificial intelligence: A systematic review. Preprint, submitted May 29, https://arxiv.org/abs/2006.00093.Google Scholar
  • Wang F, Rudin C (2015) Falling rule lists. 18th Internat. Conf. Artificial Intelligence Statist. (AISTATS) (Machine Learning Research Press, Ft. Lauderdale, FL).Google Scholar
  • Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P (2017) A Bayesian framework for learning rule sets for interpretable classification. J. Machine Learning Res. 18:1–37.Google Scholar
  • West DB (2001) Introduction to Graph Theory. 2nd ed. (Prentice Hall, Hoboken, NJ).Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.