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A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures

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  • Claudia C. Tusell-Rey

    (Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz s/n, Gustavo A. Madero, Ciudad de Mexico 07738, Mexico)

  • Oscar Camacho-Nieto

    (Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Juan de Dios Bátiz s/n, Gustavo A. Madero, Ciudad de Mexico 07700, Mexico)

  • Cornelio Yáñez-Márquez

    (Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz s/n, Gustavo A. Madero, Ciudad de Mexico 07738, Mexico)

  • Yenny Villuendas-Rey

    (Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Juan de Dios Bátiz s/n, Gustavo A. Madero, Ciudad de Mexico 07700, Mexico)

  • Ricardo Tejeida-Padilla

    (Instituto Politécnico Nacional, Escuela Superior de Turismo, Miguel Bernard 39, La Purisima Ticoman, Gustavo A. Madero, Ciudad de Mexico 07630, Mexico)

  • Carmen F. Rey Benguría

    (Centro de Estudios Educacionales “José Martí”, Universidad de Ciego de Ávila, Carretera a Morón km 9 ½, Ciego de Avila 65100, Cuba)

Abstract

In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study.

Suggested Citation

  • Claudia C. Tusell-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez & Yenny Villuendas-Rey & Ricardo Tejeida-Padilla & Carmen F. Rey Benguría, 2022. "A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures," Mathematics, MDPI, vol. 10(15), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2740-:d:878752
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    References listed on IDEAS

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    1. Yang Li & Xuewei Chao, 2020. "ANN-Based Continual Classification in Agriculture," Agriculture, MDPI, vol. 10(5), pages 1-15, May.
    2. Maryam Zaffar & Manzoor Ahmed Hashmani & K.S. Savita & Sameer Ahmad Khan, 2021. "A review on feature selection methods for improving the performance of classification in educational data mining," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 20(1/2), pages 110-131.
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