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Aumento de regresión con segmentación impulsada por datos

Shayan Alahyari1, Shiva Mehdipour Ghobadlou2, Mike Domaratzki1

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Neural networks : the official journal of the International Neural Network Society
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Este estudio presenta un nuevo marco impulsado por datos que utiliza redes generativas adversarias (GAN) y modelado de mezclas gaussianas de Mahalanobis (GMM) para abordar eficazmente los desafíos de la regresión desequilibrada identificando y aumentando muestras minoritarias.

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Área de la Ciencia:

  • Aprendizaje automático
  • Ciencia de datos
  • Inteligencia artificial

Sus antecedentes:

  • La regresión desequilibrada ocurre cuando las distribuciones objetivo están sesgadas, lo que dificulta el rendimiento del modelo en muestras subrepresentadas.
  • Los métodos actuales a menudo utilizan umbrales arbitrarios, sin lograr capturar las complejas relaciones entre características y objetivos para datos raros.
  • Esta limitación afecta a diversas aplicaciones que requieren una predicción precisa de eventos infrecuentes.

Objetivo del estudio:

  • Desarrollar un marco totalmente impulsado por datos para la regresión desequilibrada que identifique y enriquezca automáticamente las muestras minoritarias.
  • Superar las limitaciones de los umbrales fijos en las técnicas de aumento de datos existentes.
  • Mejorar el rendimiento de los modelos de aprendizaje automático en conjuntos de datos sesgados.

Principales métodos:

  • Se propone un marco de aumento basado en redes generativas adversarias (GAN).
  • Se utiliza el modelado de mezclas gaussianas de Mahalanobis (GMM) para la identificación automática de muestras minoritarias.
  • Se emplea la coincidencia determinista de vecinos más cercanos para enriquecer las regiones de datos dispersas.

Principales resultados:

  • El método propuesto identifica con éxito observaciones verdaderamente raras sin depender de umbrales preestablecidos.
  • Evaluado en 32 conjuntos de datos de referencia de regresión desequilibrada, el marco demostró un rendimiento superior.
  • Superó a las técnicas de aumento de datos de vanguardia existentes en tareas de regresión desequilibrada.

Conclusiones:

  • El marco propuesto basado en GAN con Mahalanobis-GMM ofrece una solución robusta y basada en datos para la regresión desequilibrada.
  • Este enfoque aborda eficazmente el desafío de las muestras subrepresentadas al identificarlas y aumentarlas con precisión.
  • El método muestra un potencial significativo para mejorar el rendimiento de los modelos de aprendizaje automático en escenarios de datos sesgados del mundo real.