IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v34y2022i6p2908-2929.html
   My bibliography  Save this article

bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition

Author

Listed:
  • Yanchao Liu

    (Department of Industrial and Systems Engineering, Wayne State University, Detroit, Michigan 48202)

Abstract

This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process and develops an efficient search algorithm that is able to solve practical instances of the MIP model faster than commercial solvers. The formulation is novel for it directly maximizes the Gini reduction, an effective split selection criterion that has never been modeled in a mathematical program for its nonconvexity. The proposed approach differs from other optimal classification tree models in that it does not attempt to optimize the whole tree; therefore, the flexibility of the recursive partitioning scheme is retained, and the optimization model is more amenable. The approach is implemented in an open-source R package named bsnsing. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared with trees trained by other decision tree codes including the rpart, C50, party, and tree packages in R. Compared with other optimal decision tree packages, including DL8.5, OSDT, GOSDT, and indirectly more, bsnsing stands out in its training speed, ease of use, and broader applicability without losing in prediction accuracy.

Suggested Citation

  • Yanchao Liu, 2022. "bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2908-2929, November.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:6:p:2908-2929
    DOI: 10.1287/ijoc.2022.1225
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2022.1225
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijoc.2022.1225?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lukas Tanner & Mark Schreiber & Jenny G H Low & Adrian Ong & Thomas Tolfvenstam & Yee Ling Lai & Lee Ching Ng & Yee Sin Leo & Le Thi Puong & Subhash G Vasudevan & Cameron P Simmons & Martin L Hibberd , 2008. "Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 2(3), pages 1-9, March.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    3. W. Nick Street, 2005. "Oblique Multicategory Decision Trees Using Nonlinear Programming," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 25-31, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    2. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    3. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    4. Ghosh, Atish R. & Qureshi, Mahvash S. & Kim, Jun Il & Zalduendo, Juan, 2014. "Surges," Journal of International Economics, Elsevier, vol. 92(2), pages 266-285.
      • Mahvash S Qureshi & Mr. Atish R. Ghosh & Mr. Juan Zalduendo & Mr. Jun I Kim, 2012. "Surges," IMF Working Papers 2012/022, International Monetary Fund.
    5. Tomàs Aluja-Banet & Eduard Nafria, 2003. "Stability and scalability in decision trees," Computational Statistics, Springer, vol. 18(3), pages 505-520, September.
    6. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    7. Schwartz, Ira M. & York, Peter & Nowakowski-Sims, Eva & Ramos-Hernandez, Ana, 2017. "Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience," Children and Youth Services Review, Elsevier, vol. 81(C), pages 309-320.
    8. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    9. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    10. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    11. Osman Taylan & Abdulaziz S. Alkabaa & Mustafa Tahsin Yılmaz, 2022. "Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    12. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    13. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    14. Vicente-Cera, Isaías & Acevedo-Merino, Asunción & Nebot, Enrique & López-Ramírez, Juan Antonio, 2020. "Analyzing cruise ship itineraries patterns and vessels diversity in ports of the European maritime region: A hierarchical clustering approach," Journal of Transport Geography, Elsevier, vol. 85(C).
    15. Edward Kozłowski & Anna Borucka & Andrzej Świderski & Przemysław Skoczyński, 2021. "Classification Trees in the Assessment of the Road–Railway Accidents Mortality," Energies, MDPI, vol. 14(12), pages 1-15, June.
    16. Javad Hassannataj Joloudari & Edris Hassannataj Joloudari & Hamid Saadatfar & Mohammad Ghasemigol & Seyyed Mohammad Razavi & Amir Mosavi & Narjes Nabipour & Shahaboddin Shamshirband & Laszlo Nadai, 2020. "Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model," IJERPH, MDPI, vol. 17(3), pages 1-24, January.
    17. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    18. van Riessen, B. & Negenborn, R.R. & Dekker, R., 2016. "Real-time Container Transport Planning with Decision Trees based on Offline Obtained Optimal Solutions," Econometric Institute Research Papers EI2016-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Jonathan Zufferey, 2016. "Investigating the migrant mortality advantage at the intersections of social stratification in Switzerland," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(32), pages 899-926.
    20. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:34:y:2022:i:6:p:2908-2929. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.