269 citations · 357 across the 5 of their papers we have counts for
16 papers
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Yuzhu Wu, Zhen Zhang, Gang Kou +5
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a compreh…
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews
Cristina Zuheros, Eugenio Martínez-Cámara, Enrique Herrera-Viedma +1
Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decis…
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
David Charte, Francisco Charte, María J. del Jesus +1
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate w…
A Showcase of the Use of Autoencoders in Feature Learning Applications
David Charte, Francisco Charte, María J. del Jesus +1
Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding spec…
Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise
Sergio González, Salvador García, Sheng-Tun Li +2
This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do not c…
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)
Juan Luis Suárez-Díaz, Salvador García, Francisco Herrera
Distance metric learning is a branch of machine learning that aims to learn distances from the data, which enhances the performance of similarity-based algorithms. This tutorial pr…