A Preliminary Study on Automatic Algorithm Selection for Short-Term Traffic Forecasting. - Juan S. Angarita-Zapata; Isaac Triguero; Antonio D. Masegosa (IDC, 2018)

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks. - Antonio D. Masegosa; Eneko Osaba; Juan S. Angarita-Zapata; Ibai Laña; Javier Del Ser (GECCO (Companion), 2019)

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective. - Juan S. Angarita-Zapata; Antonio D. Masegosa; Isaac Triguero (IEEE Access, 2019)

General-Purpose Automated Machine Learning for Transportation: A Case Study of Auto-sklearn for Traffic Forecasting. - Juan S. Angarita-Zapata; Antonio D. Masegosa; Isaac Triguero (IPMU (2), 2020)

A Case Study of AutoML for Supervised Crash Severity Prediction. - Juan S. Angarita-Zapata; Gina Paola Maestre-Gongora; Jenny Fajardo Calderín (IFSA/EUSFLAT/AGOP, 2021)

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective. - Juan S. Angarita-Zapata; Ainhoa Alonso-Vicario; Antonio D. Masegosa; Jon Legarda (Sensors, 2021)

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities. - Juan S. Angarita-Zapata; Gina Paola Maestre-Gongora; Jenny Fajardo Calderín (Sensors, 2021)

Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data. - Ryan Dave; Juan S. Angarita-Zapata; Isaac Triguero (CD-MAKE, 2023)

A Taxonomy of Signal Vehicle Coupled Control from a Mathematical Programming Perspective. - Arka Ghosh 001; Juan S. Angarita-Zapata; Antonio D. Masegosa; Ander Arriandiaga Laresgoiti (ITSC, 2023)

AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting. - Juan S. Angarita-Zapata; Antonio D. Masegosa; Isaac Triguero (CoRR, 2023)