Supervised Classification of Diseases Based on an Improved Associative Algorithm

Raúl Jiménez-Cruz, José-Luis Velázquez-Rodríguez, Itzamá López-Yáñez, Yenny Villuendas-Rey and Cornelio Yáñez-Márquez
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Raúl Jiménez-Cruz: Centro de Investigación en Computación del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, CDMX 07700, Mexico
José-Luis Velázquez-Rodríguez: Centro de Investigación en Computación del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, CDMX 07700, Mexico
Itzamá López-Yáñez: Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, CDMX 07700, Mexico
Yenny Villuendas-Rey: Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, CDMX 07700, Mexico
Cornelio Yáñez-Márquez: Centro de Investigación en Computación del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, GAM, CDMX 07700, Mexico

Mathematics, 2021, vol. 9, issue 13, 1-25

Abstract: The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles.

Keywords: machine learning; supervised classification; associative algorithm; diseases (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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