no code implementations • 22 Dec 2023 • David de la Rosa, Antonio J Rivera, María J del Jesus, Francisco Charte
Photo-trapping cameras are widely employed for wildlife monitoring.
1 code implementation • 26 May 2023 • Antonio J. Rivera, Miguel A. Dávila, David Elizondo, María J. del Jesus, Francisco Charte
Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios.
1 code implementation • 15 Jan 2023 • Francisco Charte, Antonio J. Rivera, Francisco Martínez, María J. del Jesus
Machine learning models work better when curated features are provided to them.
1 code implementation • 11 Nov 2021 • David Charte, Francisco Charte, Francisco Herrera
They can be applied as a preprocessing stage for a binary classification problem.
1 code implementation • 21 May 2020 • David Charte, Francisco Charte, María J. del Jesus, Francisco Herrera
All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.
1 code implementation • 8 May 2020 • David Charte, Francisco Charte, María J. del Jesus, Francisco Herrera
Autoencoders are techniques for data representation learning based on artificial neural networks.
no code implementations • 29 Nov 2018 • David Charte, Francisco Charte, Salvador García, Francisco Herrera
This field is subdivided into multiple areas, among which the best known are supervised learning (e. g. classification and regression) and unsupervised learning (e. g. clustering and association rules).
no code implementations • 23 Feb 2018 • Francisco J. Pulgar, Francisco Charte, Antonio J. Rivera, María J. del Jesus
In this work AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented.
no code implementations • 14 Feb 2018 • Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera
The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification.
no code implementations • 14 Feb 2018 • Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera
In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed.
1 code implementation • 10 Feb 2018 • Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years.
1 code implementation • 4 Jan 2018 • David Charte, Francisco Charte, Salvador García, María J. del Jesus, Francisco Herrera
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model.