Evaluating the SEMMA Methodology with AutoML Across Phishing Detection, Pollen Classification, and Voice Identification Tasks
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i4.1290Keywords:
SEMMA methodology, automated machine learning, phishing detection, pollen classification, voice identification, Support Vector Machine, metodología SEMMA, aprendizaje automático automatizado, clasificación de polenAbstract
The exponential growth of data has led to an increasing demand for effective methodologies in the field of machine learning. SEMMA (Sample, Explore, Modify, Model, and Assess) stands out among these methodologies due to its clear and efficient structure for pattern extraction. The contribution of this study lies in the application of the SEMMA methodology to a variety of case studies involving historical data, images, and audio. Specific datasets were used to address phishing detection, pollen image analysis, and voice classification in audio recordings. The results obtained highlight the effectiveness of SEMMA in solving data analysis problems across various domains and disciplines. Furthermore, potential future research directions in this field are identified.
Spanish-language metadata / Metadatos en español
Título en español:
Evaluación de la metodología SEMMA con AutoML en tareas de detección de phishing, clasificación de polen e identificación de voz
Resumen:
El crecimiento exponencial de los datos ha generado una demanda cada vez mayor de metodologías eficaces en el campo del aprendizaje automático. SEMMA (Muestrear, Explorar, Modificar, Modelar y Evaluar) destaca entre estas metodologías debido a su estructura clara y eficiente para la extracción de patrones. La contribución de este estudio radica en la aplicación de la metodología SEMMA a diversos estudios de caso que involucran datos históricos, imágenes y audio. Se utilizaron conjuntos de datos específicos para abordar la detección de phishing, el análisis de imágenes de polen y la clasificación de voz en grabaciones de audio. Los resultados obtenidos destacan la eficacia de SEMMA para resolver problemas de análisis de datos en diversos ámbitos y disciplinas. Además, se identifican posibles líneas futuras de investigación en este campo.
Palabras Claves: metodología SEMMA; aprendizaje automático automatizado; selección de modelos; detección de phishing; clasificación de polen; identificación de voz; bosque aleatorio; máquina de vectores de soporte.
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