Search Results for author: Amparo Alonso-Betanzos

Found 11 papers, 4 papers with code

Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models

no code implementations19 Jan 2024 Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Brais Cancela, Carlos Eiras-Franco

Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e. g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e. g. approximating the adequate dosage of a drug for a patient in personalized pharmacology).

Recommendation Systems regression

Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures

no code implementations28 Jul 2023 Alejandro Rodríguez-Arias, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Noelia Sánchez-Marroño

In this paper, we propose an ABM architecture that allows us to analyze the evolution of virus infections in a society based on two components: 1) an adaptation of the SEIRD model and 2) a decision-making model for citizens.

Decision Making

Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces

no code implementations9 Sep 2022 Iñigo López-Riobóo Botana, Carlos Eiras-Franco, Julio Hernandez-Castro, Amparo Alonso-Betanzos

EADMNC leverages the formulation of the previous ADMNC model to offer pre hoc and post hoc explainability, while maintaining the accuracy of the original architecture.

Anomaly Detection Network Intrusion Detection

E2E-FS: An End-to-End Feature Selection Method for Neural Networks

1 code implementation14 Dec 2020 Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos

Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and lasso variants.

Feature Importance feature selection

A scalable saliency-based Feature selection method with instance level information

1 code implementation30 Apr 2019 Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama

Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction.

feature selection

Distributed Correlation-Based Feature Selection in Spark

no code implementations31 Jan 2019 Raul-Jose Palma-Mendoza, Luis de-Marcos, Daniel Rodriguez, Amparo Alonso-Betanzos

CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains.

feature selection

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