Víctor Aceña

According to our database, Víctor Aceña authored at least 9 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Real-time classification of cattle behavior using Wireless Sensor Networks.

Jorge Navarro, Rubén R. Fernández, Víctor Aceña, Alberto Fernández-Isabel, Carmen Lancho, Isaac Martín de Diego

Internet Things, April, 2024

2023

Hostility measure for multi-level study of data complexity.

Carmen Lancho, Isaac Martín de Diego, Marina Cuesta, Víctor Aceña, Javier M. Moguerza

Appl. Intell., April, 2023

Support subsets estimation for support vector machines retraining.

Víctor Aceña, Isaac Martín de Diego, Rubén R. Fernández, Javier M. Moguerza

Pattern Recognit., 2023

2022

Minimally overfitted learners: A general framework for ensemble learning.

Víctor Aceña, Isaac Martín de Diego, Rubén R. Fernández, Javier M. Moguerza

Knowl. Based Syst., 2022

2021

A Complexity Measure for Binary Classification Problems Based on Lost Points.

Carmen Lancho, Isaac Martín de Diego, Marina Cuesta, Víctor Aceña, Javier M. Moguerza

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2021, 2021

From Classification to Visualization: A Two Way Trip.

Marina Cuesta, Isaac Martín de Diego, Carmen Lancho, Víctor Aceña, Javier M. Moguerza

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2021, 2021

2020

Random forest explainability using counterfactual sets.

Rubén R. Fernández, Isaac Martín de Diego, Víctor Aceña, Alberto Fernández-Isabel, Javier M. Moguerza

Inf. Fusion, 2020

2019

Relevance Metric for Counterfactuals Selection in Decision Trees.

Rubén R. Fernández, Isaac Martín de Diego, Víctor Aceña, Javier M. Moguerza, Alberto Fernández-Isabel

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2019, 2019

Weighted Nearest Centroid Neighbourhood.

Víctor Aceña, Javier M. Moguerza, Isaac Martín de Diego, Rubén R. Fernández

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2019, 2019