IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v150y2007i1p79-9210.1007-s10479-006-0159-8.html
   My bibliography  Save this article

Reformulation of the support set selection problem in the logical analysis of data

Author

Listed:
  • Renato Bruni

Abstract

The paper is concerned with the problem of binary classification of data records, given an already classified training set of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered. Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging results are obtained and discussed. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Renato Bruni, 2007. "Reformulation of the support set selection problem in the logical analysis of data," Annals of Operations Research, Springer, vol. 150(1), pages 79-92, March.
    • Handle: RePEc:spr:annopr:v:150:y:2007:i:1:p:79-92:10.1007/s10479-006-0159-8
    DOI: 10.1007/s10479-006-0159-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-006-0159-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-006-0159-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    2. Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lejeune, Miguel & Lozin, Vadim & Lozina, Irina & Ragab, Ahmed & Yacout, Soumaya, 2019. "Recent advances in the theory and practice of Logical Analysis of Data," European Journal of Operational Research, Elsevier, vol. 275(1), pages 1-15.
    2. Kedong Yan & Dongjing Miao & Cui Guo & Chanying Huang, 2021. "Efficient feature selection for logical analysis of large-scale multi-class datasets," Journal of Combinatorial Optimization, Springer, vol. 42(1), pages 1-23, July.

    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. Wolosewicz, Cathy & Dauzère-Pérès, Stéphane & Aggoune, Riad, 2015. "A Lagrangian heuristic for an integrated lot-sizing and fixed scheduling problem," European Journal of Operational Research, Elsevier, vol. 244(1), pages 3-12.
    2. M Diaby & A L Nsakanda, 2006. "Large-scale capacitated part-routing in the presence of process and routing flexibilities and setup costs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1100-1112, September.
    3. Ogbe, Emmanuel & Li, Xiang, 2017. "A new cross decomposition method for stochastic mixed-integer linear programming," European Journal of Operational Research, Elsevier, vol. 256(2), pages 487-499.
    4. Mutsunori Yagiura & Toshihide Ibaraki & Fred Glover, 2004. "An Ejection Chain Approach for the Generalized Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 16(2), pages 133-151, May.
    5. S Bilgin & M Azizoǧlu, 2006. "Capacity and tool allocation problem in flexible manufacturing systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 670-681, June.
    6. Weijun Xie & Yanfeng Ouyang & Sze Chun Wong, 2016. "Reliable Location-Routing Design Under Probabilistic Facility Disruptions," Transportation Science, INFORMS, vol. 50(3), pages 1128-1138, August.
    7. Peter Francis & Karen Smilowitz & Michal Tzur, 2006. "The Period Vehicle Routing Problem with Service Choice," Transportation Science, INFORMS, vol. 40(4), pages 439-454, November.
    8. Park, Moon-Won & Kim, Yeong-Dae, 2000. "A branch and bound algorithm for a production scheduling problem in an assembly system under due date constraints," European Journal of Operational Research, Elsevier, vol. 123(3), pages 504-518, June.
    9. Shangyao Yan & Chun-Ying Chen & Chuan-Che Wu, 2012. "Solution methods for the taxi pooling problem," Transportation, Springer, vol. 39(3), pages 723-748, May.
    10. Fernandez del Pozo, J. A. & Bielza, C. & Gomez, M., 2005. "A list-based compact representation for large decision tables management," European Journal of Operational Research, Elsevier, vol. 160(3), pages 638-662, February.
    11. Jenny Carolina Saldana Cortés, 2011. "Programación semidefinida aplicada a problemas de cantidad económica de pedido," Documentos CEDE 8735, Universidad de los Andes, Facultad de Economía, CEDE.
    12. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    13. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    14. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    15. Keliang Wang & Leonardo Lozano & Carlos Cardonha & David Bergman, 2023. "Optimizing over an Ensemble of Trained Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 652-674, May.
    16. Ibrahim Muter & Tevfik Aytekin, 2017. "Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 405-421, August.
    17. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    18. Raymond K. Cheung & Chung-Lun Li & Wuqin Lin, 2002. "Interblock Crane Deployment in Container Terminals," Transportation Science, INFORMS, vol. 36(1), pages 79-93, February.
    19. Ventura, Jose A. & Radhakrishnan, Sanjay, 2003. "Single machine scheduling with symmetric earliness and tardiness penalties," European Journal of Operational Research, Elsevier, vol. 144(3), pages 598-612, February.
    20. Tiwari, Richa & Jayaswal, Sachin & Sinha, Ankur, 2021. "Alternate solution approaches for competitive hub location problems," European Journal of Operational Research, Elsevier, vol. 290(1), pages 68-80.

    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:spr:annopr:v:150:y:2007:i:1:p:79-92:10.1007/s10479-006-0159-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.