All Outputs (4)

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance (2023)
Journal Article
Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024). General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance. Information Fusion, 103, Article 102135. https://doi.org/10.1016/j.inffus.2023.102135

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them.... Read More about General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.

Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data (2023)
Conference Proceeding
Dave, R., Angarita-Zapata, J. S., & Triguero, I. (2023). Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data. In Machine Learning and Knowledge Extraction (82-102). https://doi.org/10.1007/978-3-031-40837-3_6

The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry, considerable amounts of time are wasted creating bespoke applications and repetitively hand-tuning models t... Read More about Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data.

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2023). Explaining time series classifiers through meaningful perturbation and optimisation. Information Sciences, 645, Article 119334. https://doi.org/10.1016/j.ins.2023.119334

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

Identifying bird species by their calls in Soundscapes (2023)
Journal Article
Maclean, K., & Triguero, I. (2023). Identifying bird species by their calls in Soundscapes. Applied Intelligence, 53, 21485-21499. https://doi.org/10.1007/s10489-023-04486-8

In many real data science problems, it is common to encounter a domain mismatch between the training and testing datasets, which means that solutions designed for one may not transfer well to the other due to their differences. An example of such was... Read More about Identifying bird species by their calls in Soundscapes.