arXiv subjects

Jose L. Salmeron

Publications and source records attributed to Jose L. Salmeron.

9 recordsLinked to original sources

arXiv2026

Spectral Biclustering-Driven Scalability for Post-Hoc Explainability in Recommender Systems

Explainability in recommender systems is essential for ensuring transparency, accountability, and trust, yet existing post-hoc methods often encounter severe scalability challenges. Observation-level deletion diagnostics offer a counterfactual way to analyze recommendations by retraining models after removing individual users or items, but their cost grows rapidly with dataset size. To improve the practical tractability of this analysis, this paper introduces a block-deletion diagnostic framework that uses spectral biclustering to group users and items and then removes entire blocks of interactions. This formulation reduces the number of retraining procedures relative to finer-grained deletion strategies and produces explanations at the level of user segments, item groups, and their interactions. The framework is evaluated on two representative recommender paradigms, Singular Value Decomposition and Neural Collaborative Filtering, using the MovieLens and Amazon datasets. The results show that top-ranked recommendations are often more sensitive to specific interaction blocks than lower-ranked ones, with some blocks acting as supporting evidence and others having a detrimental effect on recommendation quality. The analysis also indicates that user segments differ in their sensitivity to block removal, suggesting heterogeneous levels of reliance on localized interaction patterns. These findings provide diagnostic information that is not directly visible through standard recommendation metrics. Overall, the results suggest that block-deletion diagnostics offer a practical and model-agnostic post-hoc analysis framework for recommender systems, while also highlighting that the resulting explanations depend on the chosen block structure.

cs.IR↗

arXiv2026

Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps

Fuzzy Cognitive Maps constitute a neuro-symbolic paradigm for modeling complex dynamic systems, widely adopted for their inherent interpretability and recurrent inference capabilities. However, the standard FCM formulation, characterized by scalar synaptic weights and monotonic activation functions, is fundamentally constrained in modeling non-monotonic causal dependencies, thereby limiting its efficacy in systems governed by saturation effects or periodic dynamics. To overcome this topological restriction, this research proposes the Kolmogorov-Arnold Fuzzy Cognitive Map (KA-FCM), a novel architecture that redefines the causal transmission mechanism. Drawing upon the Kolmogorov-Arnold representation theorem, static scalar weights are replaced with learnable, univariate B-spline functions located on the model edges. This fundamental modification shifts the non-linearity from the nodes' aggregation phase directly to the causal influence phase. This modification allows for the modeling of arbitrary, non-monotonic causal relationships without increasing the graph density or introducing hidden layers. The proposed architecture is validated against both baselines (standard FCM trained with Particle Swarm Optimization) and universal black-box approximators (Multi-Layer Perceptron) across three distinct domains: non-monotonic inference (Yerkes-Dodson law), symbolic regression, and chaotic time-series forecasting. Experimental results demonstrate that KA-FCMs significantly outperform conventional architectures and achieve competitive accuracy relative to MLPs, while preserving graph- based interpretability and enabling the explicit extraction of mathematical laws from the learned edges.

cs.AI↗

arXiv2025

A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research

Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.

cs.AI↗

arXiv2024

Blind Federated Learning without initial model

Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine learning model using sensitive data from different sources, such as hospitals. In this paper, the authors propose two innovative methodologies for Particle Swarm Optimisation-based federated learning of Fuzzy Cognitive Maps in a privacy-preserving way. In addition, one relevant contribution this research includes is the lack of an initial model in the federated learning process, making it effectively blind. This proposal is tested with several open datasets, improving both accuracy and precision.

cs.LG↗

arXiv2024

Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data

The increasing requirements for data protection and privacy has attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learning the architecture was centralised and the aggregation was done with federated averaging, meaning that a central server will orchestrate the federation using the most straightforward averaging strategy. This research is focused on testing different federated strategies in a peer-to-peer environment. The authors propose various aggregation strategies for federated learning, including weighted averaging aggregation, using different factors and strategies based on participant contribution. The strategies are tested with varying data sizes to identify the most robust ones. This research tests the strategies with several biomedical datasets and the results of the experiments show that the accuracy-based weighted average outperforms the classical federated averaging method.

cs.LG↗

arXiv2024

A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets

Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data.

cs.LG↗

arXiv2007

Redesigning Decision Matrix Method with an indeterminacy-based inference process

For academics and practitioners concerned with computers, business and mathematics, one central issue is supporting decision makers. In this paper, we propose a generalization of Decision Matrix Method (DMM), using Neutrosophic logic. It emerges as an alternative to the existing logics and it represents a mathematical model of uncertainty and indeterminacy. This paper proposes the Neutrosophic Decision Matrix Method as a more realistic tool for decision making. In addition, a de-neutrosophication process is included.

cs.AI↗

arXiv2006

Computational Modeling in Applied Problems: collected papers on econometrics, operations research, game theory and simulation

Computational models pervade all branches of the exact sciences and have in recent times also started to prove to be of immense utility in some of the traditionally 'soft' sciences like ecology, sociology and politics. This volume is a collection of a few cutting-edge research papers on the application of variety of computational models and tools in the analysis, interpretation and solution of vexing real-world problems and issues in economics, management, ecology and global politics by some prolific researchers in the field.

cs.OH↗

arXiv2005

Processing Uncertainty and Indeterminacy in Information Systems success mapping

IS success is a complex concept, and its evaluation is complicated, unstructured and not readily quantifiable. Numerous scientific publications address the issue of success in the IS field as well as in other fields. But, little efforts have been done for processing indeterminacy and uncertainty in success research. This paper shows a formal method for mapping success using Neutrosophic Success Map. This is an emerging tool for processing indeterminacy and uncertainty in success research. EIS success have been analyzed using this tool.

cs.AI↗