Redes Neuronales Artificiales en la Resolución de Problemas de Clasificación: Una Revisión Sistemática de la Literatura
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https://doi.org/10.29019/enfoqueute.1058Palabras clave:
redes neuronales artificiales, clasificación, aprendizaje automático, arquitectura redes neuronales, minería de datosResumen
Las redes neuronales artificiales (ANNs) se han convertido en herramientas indispensables para resolver tareas de clasificación en diversos dominios. Esta revisión sistemática de la literatura explora el panorama de la utilización de ANN en la clasificación, abordando tres preguntas clave de investigación: los tipos de arquitecturas empleadas, su precisión y los datos utilizados. La revisión abarca 30 estudios publicados entre 2019 y 2024, revelando las Redes Neuronales Convolucionales (CNNs) como la arquitectura predominante en tareas relacionadas con imágenes, seguidas por las arquitecturas de Perceptrón Multicapa (MLP) para tareas de clasificación en general. Las Redes Neuronales de Propagación Hacia Adelante (FFNN) exhibieron la mayor precisión promedio con un 97.12 %, con estudios específicos logrando resultados excepcionales en diversas tareas de clasificación. Además, la revisión identifica las imágenes digitalizadas como una fuente de datos comúnmente utilizada, reflejando la amplia aplicabilidad de las ANN en tareas como el diagnóstico médico y la teledetección. Los hallazgos subrayan la importancia de los enfoques de aprendizaje automático, destacan la robustez de las ANN en lograr una alta precisión y sugieren caminos para investigaciones futuras para mejorar la interpretabilidad, eficiencia y capacidades de generalización, así como abordar desafíos relacionados con la calidad de los datos.
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