Associative classification with multiobjective tabu search

Beausoleil, Ricardo P.; Beausoleil, Ricardo P.

doi:10.15517/rmta.v27i2.42438artículo

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Revista de Matemática Teoría y Aplicaciones

versão impressa ISSN 1409-2433

Rev. Mat vol.27 no.2 San José Jul./Dez. 2020

http://dx.doi.org/10.15517/rmta.v27i2.42438artículo

Artículo

Associative classification with multiobjective tabu search

Clasificación asociativa con búsqueda tabú multiobjetivo

Ricardo P. Beausoleil¹

^¹Instituto de Cibernética Matemática y Física, Departamento de Matemática Interdisciplinaria, La Habana, Cuba; rbeausol@icimaf.cu

Abstract

This paper presents an application of Tabu Search algorithm to association rule mining. We focus our attention specifically on classification rule mining, often called associative classification, where the consequent part of each rule is a class label. Our approach is based on seek a rule set handled as an individual. A Tabu search algorithm is used to search for Pareto-optimal rule sets with respect to some evaluation criteria such as accuracy and complexity. We apply a called Apriori algorithm for an association rules mining and then a multiobjective tabu search to a selection rules. We report experimental results where the effect of our multiobjective selection rules is examined for some well-known benchmark data sets from the UCI machine learning repository.

Keywords: combinatorial data analysis; associative classification; tabu search; multiobjective optimization.

Resumen

Este artículo presenta una aplicación de Búsqueda Tabu Multiobjetivo a la minería de reglas de asociación. Centramos nuestra atención específicamente en la minería de reglas de clasificación, frecuentemente llamada clasificación asociativa, donde la parte consecuente es una clase. Nuestro enfoque se basa en la búsqueda de un conjunto de reglas manipulado como un individuo para la clasificación. Un algoritmo de Búsqueda Tabu es utilizado para encontrar conjuntos de reglas Pareto-Óptimo con respecto a algunos criterios tales como exactitud y complejidad. Aplicamos el siguiente algoritmo de A priori para la extracción de las reglas de asociación del problema en cuestión y entonces una búsqueda Tabu multiobjetivo es realizada para seleccionar subconjuntos de reglas. Reportamos experimentos donde es examinado el efecto de la selección multiobjetivo para algunos conjuntos de datos bien conocidos de la base de datos del almacén de máquinas de aprendizaje de la UCI.

Palabras clave: análisis de datos combinatorio; clasificación asociativa; búsqueda tabú; optimización multiobjectivo.

Mathematics Subject Classification: 90C27, 90C29, 90C30, 90B50, 93B40.

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Received: June 25, 2019; Revised: November 11, 2019; Accepted: February 06, 2020

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