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A pool-based pattern generation algorithm for logical analysis of data with automatic fine-tuning

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  • Caserta, Marco
  • Reiners, Torsten

Abstract

In this paper, we address the binary classification problem, in which one is given a set of observations, characterized by a number of (binary and non-binary) attributes and wants to determine which class each observation belongs to. The proposed classification algorithm is based on the Logical Analysis of Data (LAD) technique and belongs to the class of supervised learning algorithms. We introduce a novel metaheuristic-based approach for pattern generation within LAD. The key idea relies on the generation of a pool of patterns for each given observation of the training set. Such a pool is built with one or more criteria in mind (e.g., diversity, homogeneity, coverage, etc.), and is paramount in the achievement of high classification accuracy, as shown by the computational results we obtained. In addition, we address one of the major concerns of many data mining algorithms, i.e., the fine-tuning and calibration of parameters. We employ here a novel technique, called biased Random-Key Genetic Algorithm that allows the calibration of all the parameters of the algorithm in an automatic fashion, hence reducing the fine-tuning effort required and enhancing the performance of the algorithm itself. We tested the proposed approach on 10 benchmark instances from the UCI repository and we proved that the algorithm is competitive, both in terms of classification accuracy and running time.

Suggested Citation

  • Caserta, Marco & Reiners, Torsten, 2016. "A pool-based pattern generation algorithm for logical analysis of data with automatic fine-tuning," European Journal of Operational Research, Elsevier, vol. 248(2), pages 593-606.
  • Handle: RePEc:eee:ejores:v:248:y:2016:i:2:p:593-606
    DOI: 10.1016/j.ejor.2015.05.078
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    References listed on IDEAS

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    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. Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    3. Maurizio Boccia & Antonio Sforza & Claudio Sterle, 2020. "Simple Pattern Minimality Problems: Integer Linear Programming Formulations and Covering-Based Heuristic Solving Approaches," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1049-1060, October.
    4. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    5. Caserta, Marco & Voß, Stefan, 2019. "The robust multiple-choice multidimensional knapsack problem," Omega, Elsevier, vol. 86(C), pages 16-27.

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