Search Results for author: Daniel Peralta

Found 8 papers, 0 papers with code

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions

no code implementations28 Feb 2023 Germán González-Almagro, Daniel Peralta, Eli de Poorter, José-Ramón Cano, Salvador García

To remedy this, this study presents in-detail the background of constrained clustering and provides a novel ranked taxonomy of the types of constraints that can be used in constrained clustering.

Constrained Clustering

Polar Encoding: A Simple Baseline Approach for Classification with Missing Values

no code implementations4 Oct 2022 Oliver Urs Lenz, Daniel Peralta, Chris Cornelis

We propose polar encoding, a representation of categorical and numerical $[0, 1]$-valued attributes with missing values to be used in a classification context.

Attribute Denoising +1

No imputation without representation

no code implementations28 Jun 2022 Oliver Urs Lenz, Daniel Peralta, Chris Cornelis

Imputation allows datasets to be used with algorithms that cannot handle missing values by themselves.

Imputation

Optimised one-class classification performance

no code implementations4 Feb 2021 Oliver Urs Lenz, Daniel Peralta, Chris Cornelis

The hyperparameters of SVM and LOF have to be optimised through cross-validation, while NND, LNND and ALP allow an efficient form of leave-one-out validation and the reuse of a single nearest-neighbour query.

Classification General Classification +1

Average Localised Proximity: A new data descriptor with good default one-class classification performance

no code implementations26 Jan 2021 Oliver Urs Lenz, Daniel Peralta, Chris Cornelis

One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive examples of that class, and no counter-examples.

Classification General Classification +1

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

no code implementations21 Mar 2017 Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, Jose M. Benitez, Francisco Herrera

In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction.

Classification General Classification +1

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