DIVERSITY-BASED SELECTION OF LEARNING ALGORITHMS: A BAGGING APROACH

Nowadays, classification problems are becoming increasingly important in many real-world applications. As the problems become more complex and the consequences of a bad decision are more serious, more advanced techniques, as the combination of classifiers, need to be applied. When combining classifi...

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Published in:Investigación operacional 2021-10, Vol.42 (4), p.495
Main Authors: Cabrera-Hernandez, Leidys, Hernandez, Alejandro Morales, Gomez, Maricel Meneses, Marcel, Alfredo Meneses, Cardoso, Gladys M. Casas, Lorenzo, Maria M. Garcia
Format: Article
Language:English
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Summary:Nowadays, classification problems are becoming increasingly important in many real-world applications. As the problems become more complex and the consequences of a bad decision are more serious, more advanced techniques, as the combination of classifiers, need to be applied. When combining classifiers, it is important to ensure diversity between them as it does not make sense to combine classifiers whose classification is the same. There are several techniques to ensure diversity in systems like these and generally it consider modify the data set, use different learning algorithms or make a process of improvement or learning on the individual classification. Although the relationship between diversity and system accuracy has not been fully established, it is clear that diversity remains a factor to be taken into account in the construction of multiclassifiers. In this paper we present a modification to the bagging algorithm to consider different learning algorithms during the training process and optimize the classifiers built to obtain diverse systems and as accurate as possible. Executed simulations suggest the use of the Double Failure pairwise measure to quantify the diversity of the system. With respect to the number of classifiers used, it was observed that the systems built had approximately half of the total classifiers they should have. After, the superiority of the proposed method with respect to five state-of-the-art multiclassifiers was verified and it is suggested the incorporation of a learning process like the one executed in Stacking. Finally, are shown results in biochemical real applications and the general conclusions are exposed. KEYWORDS: Diversity measures, classifiers combination, Bagging, supervised learning. MSC: 68T05 En la actualidad, los problemas de clasificación cada día cobran mayor importancia en muchas aplicaciones reales. A medida que los problemas se hacen más complejos y las consecuencias de una mala decisión son más graves se necesita aplicar técnicas más avanzadas como la combinación de clasificadores. Cuando se combinan clasificadores es importante garantizar la diversidad entre ellos ya que no tiene sentido combinar clasificadores cuya clasificación sea la misma. Existen varias técnicas para garantizar la diversidad en sistemas de este tipo y de forma general consideran modificar el conjunto de datos, utilizar diferentes algoritmos de aprendizaje o efectuar un proceso de mejora o aprendizaje sobre la clasificación i
ISSN:0257-4306