@article{TRONCOSO-GARCIA23,
title = {A new approach based on association rules to add explainability to time series forecasting models},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523000295},
doi = {10.1016/j.inffus.2023.01.021},
year = {2023},
date = {2023-01-22},
journal = {Information Fusion},
volume = {94},
pages = {169-180},
abstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{TRONCOSO-GARCIA22,
title = {Explainable machine learning for sleep apnea prediction},
author = {A. R. Troncoso-García and M. Martínez-Ballesteros and F. Martínez-Álvarez and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922012406},
doi = {https://doi.org/10.1016/j.procs.2022.09.351},
year = {2022},
date = {2022-09-10},
booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems},
pages = {2930-2939},
abstract = {Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. Data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{TRONCOSO-GARCIA21,
title = {Non-invasive devices for respiratory sound monitoring},
author = {A. R. Troncoso-García and J. A. Ortega and R. Seepold and N. Martínez-Madrid},
url = {https://www.sciencedirect.com/science/article/pii/S1877050921018135},
doi = {https://doi.org/10.1016/j.procs.2021.09.076},
year = {2021},
date = {2021-01-01},
booktitle = {KES International Conference on Knowledge Based and Intelligent information and Engineering Systems},
pages = {3040-3048},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}