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

Claudia C. Tusell-Rey, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez, Yenny Villuendas-Rey, Ricardo Tejeida-Padilla and Carmen F. Rey Benguría
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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

Mathematics, 2022, vol. 10, issue 15, 1-19

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.

Keywords: business; classification; meta-learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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