Parallel model of online sequential extreme learning machines for classification problems with large-scale databases

• Autores: Gelvez-Almeida, Elkin; Barrientos, Ricardo J.; Vilches-Ponce, Karina; Mora, Marco
• Año de publicación: 2023
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Parallel computing; High performance computing; Extreme learning machine; Fingerprint classification
• Institución: Universidad Nacional de La Plata

Nowadays, the sizes of databases in real-world applications are around TeraByte or PetaByte. Therefore, training neural networks in reasonable times is challenging and requires high-cost computational architectures. OS-ELM is a variant of ELM, proposed for real-world applications. This algorithm allows training with new data using the previous results without reusing the previous dataset. In this work, we present a parallel model of OS-ELM for classification problems using large-scale databases. The model consists of training several OS-ELM using multithreaded programming. The training dataset is distributed according to the number of working threads. Then, the test dataset is classified by all pre-trained OS-ELMs. Finally, the test dataset is classified using a frequency criterion. Preliminary results show that increasing the number of threads decreases the training time without significantly affecting the test accuracy of each OS-ELM.
Facultad de Informática