Class imbalance may reduce the classifier performance in several recognition pattern problems. Such negative effect is more notable with least represented class (minority class) Patterns. A strategy for handling this problem consisted of treating the classes included in this problem separately (majority and minority classes) to balance the data sets (DS). This paper has studied high sensitivity to class imbalance shown by an associative model of classification: hybrid associative classifier with translation (HACT); imbalanced DS impact on associative model performance was studied. The convenience of using sub-sampling methods for decreasing imbalanced negative effects on associative memories was analysed. This proposal's feasibility was based on experimental results obtained from eleven real-world datasets.

Resumen. Usando un robot humanoide Nao, proponemos apoyar a niños que tienen una deficiencia de color específico llamado "daltonismo" que evita que este grupo minoritario esté alerta de posibles advertencias visuales en juegos, parques y zoológicos. La relevancia de nuestro estudio radica en el apoyo en situaciones de peligro por parte de niños con daltonismo, identificar amenazas con colores específicos y ayudar a los niños con daltonismo en entornos visuales, como los entornos asociados con ciudades inteligentes. Palabras clave: Niños con ceguera del color, reconocimiento de patrones, robot humanoide NAO.