Álvar Arnaiz-González

Orcid: 0000-0001-6965-0237

According to our database1, Álvar Arnaiz-González authored at least 28 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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Bibliography

2024
Correction: Towards automatic phytolith classification using feature extraction and combination strategies.
Prog. Artif. Intell., September, 2024

Towards automatic phytolith classification using feature extraction and combination strategies.
Prog. Artif. Intell., September, 2024

An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data.
Inf., April, 2024

Personalising the Training Process with Adaptive Virtual Reality: A Proposed Framework, Challenges, and Opportunities.
Proceedings of the Extended Reality - International Conference, 2024

2023
Detection of Stress Stimuli in Learning Contexts of iVR Environments.
Proceedings of the Extended Reality - International Conference, 2023

2022
Correction to: Rotation Forest for multi-target regression.
Int. J. Mach. Learn. Cybern., 2022

Rotation Forest for multi-target regression.
Int. J. Mach. Learn. Cybern., 2022

When is resampling beneficial for feature selection with imbalanced wide data?
Expert Syst. Appl., 2022

2021
Rotation Forest for Big Data.
Inf. Fusion, 2021

Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark.
Neurocomputing, 2021

Experimental evaluation of ensemble classifiers for imbalance in Big Data.
Appl. Soft Comput., 2021

2020
Random Balance ensembles for multiclass imbalance learning.
Knowl. Based Syst., 2020

An experimental evaluation of mixup regression forests.
Expert Syst. Appl., 2020

Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale.
CoRR, 2020

2019
Instance selection improves geometric mean accuracy: a study on imbalanced data classification.
Prog. Artif. Intell., 2019

Evolutionary prototype selection for multi-output regression.
Neurocomputing, 2019

2018
A taxonomic look at instance-based stream classifiers.
Neurocomputing, 2018

Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning.
Expert Syst. Appl., 2018

Seshat - a web-based educational resource for teaching the most common algorithms of lexical analysis.
Comput. Appl. Eng. Educ., 2018

Local sets for multi-label instance selection.
Appl. Soft Comput., 2018

2017
MR-DIS: democratic instance selection for big data by MapReduce.
Prog. Artif. Intell., 2017

MR-DIS - A Scalable Instance Selection Algorithm using MapReduce on Spark.
ERCIM News, 2017

2016
Random feature weights for regression trees.
Prog. Artif. Intell., 2016

Instance selection of linear complexity for big data.
Knowl. Based Syst., 2016

Fusion of instance selection methods in regression tasks.
Inf. Fusion, 2016

Instance selection for regression: Adapting DROP.
Neurocomputing, 2016

Instance selection for regression by discretization.
Expert Syst. Appl., 2016

2015
An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees.
Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015


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