Agregación de índices de calidad basados en redes en el problema de localización de comercios minoristasUn enfoque desde el aprendizaje supervisado

  1. Virginia Ahedo 1
  2. José Ignacio Santos 1
  3. José Manuel Galán 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Revue:
Dirección y organización: Revista de dirección, organización y administración de empresas

ISSN: 1132-175X

Année de publication: 2024

Número: 83

Pages: 5-17

Type: Article

DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Dirección y organización: Revista de dirección, organización y administración de empresas

Résumé

In retailing, the location problem is a fundamental strategic aspect. It is usually formalized as a multi-criteria optimization problem to choose the most appropriate spot. A relevant element in the selection is the adequacy of the commercial ecosystem in the vicinity of the location. To account for this criterion, there are different primary indices based on networks that formalize the quality of the available options with regard to the surrounding ecosystem. Previous research suggests that aggregating the different indices using a classifier can improve the quality of these metrics. In this paper, we compare different classifiers to assess their performance in that respect. The analysis has been performed in a context of transfer knowledge and information fusion using data from all the cities in Castile and Leon, Spain. Our results show that the random forest and generalized linear models obtain results significantly superior to other alternatives

Références bibliographiques

  • AHEDO, V., SANTOS, J. I. & GALAN, J. M. (2021). «Knowledge Transfer in Commercial Feature Extraction for the Retail Store Location Problem». IEEE Access, 9, pp. 132967–132979, doi: 10.1109/ ACCESS.2021.3115712.
  • AHEDO, V., SANTOS, J. I. & GALÁN, J. M. (2023). «Combining Quality Indexes in the Retail Location Problem Using Generalized Linear Models». In: Lecture Notes on Data Engineering and Communications Technologies. Springer, pp. 47–52, doi: 10.1007/978-3- 031-27915-7_9
  • BENJAMİNİ, Y. & YEKUTİELİ, D. (2001). «The Control of the False Discovery Rate in Multiple Testing under Dependency». The Annals of Statistics, 29(4), pp. 1165– 1188.
  • BERGSTRA, J. & BENGİO, Y. (2012). «Random Search for Hyper-Parameter Optimization». J. Mach. Learn. Res., 13(null), pp. 281–305.
  • BERMAN, B. R., EVANS, J. R. & CHATTERJEE, P. M. (2018). Retail Management. A Strategic Approach. Pearson.
  • BREİMAN, L. (2001). «Random Forests». Machine Learning, 45(1), pp. 5–32, doi: 10.1023/A:1010933404324.
  • CHEN, Y. M., CHEN, T. Y. & CHEN, L. C. (2020). «On a method for location and mobility analytics using location-based services: a case study of retail store recommendation». Online Information Review, doi: 10.1108/OIR-10-2017-0292.
  • ÇOBAN, V. (2020). «Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation». Complex & Intelligent Systems, 6(3), pp. 507–529, doi: 10.1007/s40747-020- 00152-5.
  • FRİEDMAN, J. H. (2001). «Greedy Function Approximation: A Gradient Boosting Machine». Annals of Statistics, 29(5), pp. 1189–1232.
  • GHASEMİAN, A., HOSSEİNMARDİ, H., GALSTYAN, A., AİROLDİ, E. M. & CLAUSET, A. (2020). «Stacking models for nearly optimal link prediction in complex networks». Proceedings of the National Academy of Sciences of the United States of America, 117(38), pp. 23393–23400, doi: 10.1073/pnas.1914950117.
  • GÓMEZ, S., JENSEN, P. & ARENAS, A. (2009). «Analysis of community structure in networks of correlated data». Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(1), p. 16114, doi: 10.1103/ PhysRevE.80.016114.
  • H2O.Aİ (2020). h2o: R Interface for H2O. R package version 3.30.0.6. https://github.com/h2oai/h2o-3.
  • HASTİE, T., TİBSHİRANİ, R. & FRİEDMAN, J. (2009). The Elements of Statistical Learning. 2nd ed. New York, NY: Springer.
  • JAMES, G., WİTTEN, D., HASTİE, T. & TİBSHİRANİ, R. (2013). An Introduction to Statistical Learning. First. Springer Science & Business Media.
  • JENSEN, P. (2006). «Network-based predictions of retail store commercial categories and optimal locations». Physical Review E, 74(3), p. 035101, doi: 10.1103/ PhysRevE.74.035101.
  • JENSEN, P. (2009). «Analyzing the Localization of Retail Stores with Complex Systems Tools». In: Adams, N. M., Robardet, C., Siebes, A., & Boulicaut, J.-F. (eds.) Advances in Intelligent Data Analysis VIII. Springer Berlin Heidelberg, pp. 10–20, doi: 10.1007/978-3-642- 03915-7_2.
  • KARAMSHUK, D., NOULAS, A., SCELLATO, S., NİCOSİA, V. & MASCOLO, C. (2013). «Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement». In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 793–801, doi: 10.1145/2487575.2487616.
  • KONİSHİ, H. (2005). «Concentration of competing retail stores». Journal of Urban Economics, 58(3), pp. 488– 512, doi: 10.1016/j.jue.2005.08.005.
  • KRUGMAN, P. (1991). Geography and Trade. London, UK: MIT Press.
  • KUPERVASSER, O. (2014). «The mysterious optimality of Naive Bayes: Estimation of the probability in the system of “classifiers”». Pattern Recognition and Image Analysis, 24(1), pp. 1–10, doi: 10.1134/ S1054661814010088.
  • LİN, J., OENTARYO, R., LİM, E.-P., VU, C., VU, A. & KWEE, A. (2016). « Where is the Goldmine?: Finding Promising Business Locations through Facebook Data Analytics». In: Proceedings of the 27th ACM Conference on Hypertext and Social Media - HT ’16. New York, New York, USA: ACM Press, pp. 93–102, doi: 10.1145/2914586.2914588.
  • MARTİN, O., AHEDO, V., SANTOS, J. I. & GALAN, J. M. (2022). «Comparative study of classification algorithms for quality assessment of resistance spot welding joints from pre- and post-welding inputs». IEEE Access, pp. 1–1, doi: 10.1109/ACCESS.2022.3142515.
  • MATLOCK, K., DE NİZ, C., RAHMAN, R., GHOSH, S. & PAL, R. (2018). «Investigation of model stacking for drug sensitivity prediction». BMC Bioinformatics, 19(Suppl 3), p. 71, doi: 10.1186/s12859-018-2060-2.
  • RADEV, D. R., Qİ, H., WU, H. & FAN, W. (2002). « Evaluating Web-based Question Answering Systems». In: Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02). Las Palmas, Spain: European Language Resources Association.
  • SÁNCHEZ-SAİZ, R.M., GALÁN, J. M. & SANTOS, J. I. (2014). «Localization Based on Business Interactions Through a Simulated Annealing Algorithm». In: Managing Complexity. Springer, pp. 325–331, doi: 10.1007/978-3-319-04705-8_38.
  • SÁNCHEZ-SAİZ, R. M., AHEDO, V., SANTOS, J. I., GÓMEZ, S. & GALÁN, J. M. (2021). «Dataset of the retailing location networks in the cities of Castile-Leon, Madrid and Barcelona», doi: 10.36443/10259/5585.
  • SÁNCHEZ-SAİZ, R. M., AHEDO, V., SANTOS, J. I., GÓMEZ, S. & GALÁN, J. M. (2022). «Identification of robust retailing location patterns with complex network approaches». Complex & Intelligent Systems, 8(1), pp. 83–106, doi: 10.1007/s40747-021-00335-8.
  • SHAİKH, S. A., MEMON, M. A., PROKOP, M. & KİM, K. S. (2020). «An AHP/TOPSIS-based approach for an optimal site selection of a commercial opening utilizing geospatial data». Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, pp. 295–302, doi: 10.1109/ BigComp48618.2020.00-58.
  • TİBSHİRANİ, R. (1996). «Regression Selection and Shrinkage via the Lasso». Journal of the Royal Statistical Society: Series B (Statistical Methodology), 58(1), pp. 267–288, doi: 10.2307/2346178.
  • VOORHEES, E. M. (1999). «TREC-8 Question Answering Track Report». In: Proceedings of the 8th text retrieval conference. National Institute of Standards and Technology, pp. 77–82.
  • WOLPERT, D. (1992). «Stacked Generalization». Neural Networks, 5, pp. 241–259.
  • WOLPERT, D. H. (2002). «The Supervised Learning NoFree-Lunch Theorems». In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., & Hoffman, F. (eds.) Soft Computing and Industry. London: Springer London, pp. 25–42.
  • XU, M., WANG, T., WU, Z., ZHOU, J., Lİ, J. & WU, H. (2016). «Store Location Selection via Mining Search Query Logs of Baidu Maps», doi: 10.48550/ arXiv.1606.03662.
  • ZENTES, J., MORSCHETT, D. & SCHRAMM-KLEİN, H. (2012). Strategic Retail Management. Wiesbaden: Gabler Verlag.
  • ZOU, H. & HASTİE, T. (2005). «Regularization and variable selection via the elastic net». Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), pp. 301–320, doi: 10.1111/j.1467- 9868.2005.00503.x.
La source de données: Dialnet