Classifier fusion results using various open literature data sets

Lynch, Robert S.; Willett, Peter

doi:10.1109/icsmc.2003.1243900

Cited by 2 publications

(1 citation statement)

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“…Un multiclasificador es un conjunto de clasificadores que combinan sus predicciones siguiendo un determinado esquema, con el fin de obtener una predicción más fiable que la que normalmente serían capaces de obtener en solitario. La combinación de clasificadores ha sido abordada en la literatura a través de distintos términos, entre ellos: ensamblados (ensembles) [17], [18]; modelos múltiples (multiple models) [19], [20]; sistemas de múltiples clasificadores (multiple classifier systems) [21]; combinación de clasificadores (combining classifiers) [22]; integración de clasificadores (integration of classifiers) [23]; mezcla de expertos (mixture of experts) [24], [25]; comité de decisión (decision committee) [26]; comité de expertos (committee of experts); fusión de clasificadores (classifier fusion) [27], [28] y aprendizaje multimodelo (multimodel learning).…”

Section: Trabajos Relacionadosunclassified

Minería de Patrones Secuenciales aplicada a la Predicción del Plegamiento de Proteínas

Quintana-Zaez¹,

Calderón-Ruiz²,

Santisteban-Toca³

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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Sequence mining consists of finding statistically relevant patterns in data collections represented sequentially. These, are an important type of data, where it matters the order that occupy the elements in the set and that finds a wide range of applications in Bioinformatics and Computational Biology. The prediction of protein structures is one of these applications. Where, a protein is no more than a sequence of amino acids forming patterns known as alpha helices, beta sheets and turns. For purposes of our investigation, these collections or secondary structures would be the itemsets, while the amino acids that make up the entire sequence, the items. Despite multiple attempts to predict protein folding, the algorithms developed to date only reach a 35% effectiveness. That is why we propose SPMCcm, an algorithm based on the prediction of frequent sequences and a scheme of classifiers. Which uses the information provided by the amino acid sequence, in two stages. Where, the first stage learns of the interactions between the secondary structures of the proteins, which it extracts as frequent sequences or itemsets. Meanwhile, the second stage learns of the interaction between the amino acids present in the interacting structures or items.The experimental evaluation showed that SPMCcm behaves in a similar way, independently of the base classifier used, reaching accuracies in the prediction of up to 48%, higher than the 35% reported by the literature, without using large computational resources and possessing explanatory capacity.

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