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Logical analysis of multiclass data with relaxed patterns

Author

Listed:
  • Travaughn C. Bain

    (Florida Institute of Technology)

  • Juan F. Avila-Herrera

    (Universidad Nacional Escuela de Matemática)

  • Ersoy Subasi

    (Florida Institute of Technology)

  • Munevver Mine Subasi

    (Florida Institute of Technology)

Abstract

An efficient and robust algorithm based on mixed integer linear programming is proposed to extend the Logical Analysis of Data (LAD) methodology to solve multiclass classification problems, where One-vs-Rest learning models are constructed to classify observations in predefined classes. The proposed algorithm uses two control parameters, homogeneity and prevalence, for identifying relaxed (fuzzy) patterns in multiclass datasets. The utility of the proposed method is demonstrated through experiments on multiclass benchmark datasets. Numerical experiments show that the efficiency and performance of the proposed multiclass LAD method with relaxed patterns is comparable to, if not better than, those of the previously developed LAD based multiclass classification as well as other well-known supervised learning methods.

Suggested Citation

  • Travaughn C. Bain & Juan F. Avila-Herrera & Ersoy Subasi & Munevver Mine Subasi, 2020. "Logical analysis of multiclass data with relaxed patterns," Annals of Operations Research, Springer, vol. 287(1), pages 11-35, April.
    • Handle: RePEc:spr:annopr:v:287:y:2020:i:1:d:10.1007_s10479-019-03389-7
    DOI: 10.1007/s10479-019-03389-7
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    References listed on IDEAS

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    1. Peter Hammer & Tibérius Bonates, 2006. "Logical analysis of data—An overview: From combinatorial optimization to medical applications," Annals of Operations Research, Springer, vol. 148(1), pages 203-225, November.
    2. P. Hammer & A. Kogan & M. Lejeune, 2011. "Reverse-engineering country risk ratings: a combinatorial non-recursive model," Annals of Operations Research, Springer, vol. 188(1), pages 185-213, August.
    3. A.B. Hammer & P.L. Hammer & I. Muchnik, 1999. "Logical analysis of Chinese labor productivity patterns," Annals of Operations Research, Springer, vol. 87(0), pages 165-176, April.
    4. Pierre Lemaire, 2011. "Extensions of Logical Analysis of Data for growth hormone deficiency diagnoses," Annals of Operations Research, Springer, vol. 186(1), pages 199-211, June.
    5. Sorin Alexe & Eugene Blackstone & Peter Hammer & Hemant Ishwaran & Michael Lauer & Claire Pothier Snader, 2003. "Coronary Risk Prediction by Logical Analysis of Data," Annals of Operations Research, Springer, vol. 119(1), pages 15-42, March.
    6. Miguel Lejeune & François Margot, 2011. "Optimization for simulation: LAD accelerator," Annals of Operations Research, Springer, vol. 188(1), pages 285-305, August.
    7. Dupuis, Christine & Gamache, Michel & Pagé, Jean-François, 2012. "Logical analysis of data for estimating passenger show rates at Air Canada," Journal of Air Transport Management, Elsevier, vol. 18(1), pages 78-81.
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    Cited by:

    1. 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.

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