Salvador García

Universidad de Granada

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Francisco Herrera

Universidad de Granada

210

Julián Luengo

Universidad de Granada

Isaac Triguero

Universidad de Granada

José-Ramón Cano

Universidad de Jaén

Alberto Fernández

Universidad de Granada

Sergio Ramírez‐Gallego

Universidad de Granada

Diego García‐Gil

Instituto Andaluz de Ciencias de la Tierra

Joaquín Derrac

Cardiff University

Bartosz Krawczyk

Virginia Commonwealth University

Mikel Galar

Universidad Publica de Navarra

Cooperative Institutions

Universidad de Granada

Instituto Andaluz de Ciencias de la Tierra

John Wiley & Sons (United States)

Universidad de Málaga

Universidad Complutense de Madrid

Universidad de Jaén

Universidad Politécnica de Madrid

Software (Spain)

Intelligent Systems Research (United States)

University of Córdoba

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Cdi Panamá de 2010, Art. 28: Entrada en Vigor

Leyes Españolas (2014)

Salvador García

Panama

Source

Cite

Citations (0)

A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms

Salvador García Joaquín Derrac Julián Luengo Francisco Herrera

The nested generalized exemplar theory accomplishes learning by storing objects in Euclidean n-space, as hyperrectangles. Classification of new data is performed by computing their distance to the nearest "generalized exemplar" or hyperrectangle. This learning method permits to combine the distance-based classification with the axis-parallel rectangle representation employed in most of the rule-learning systems. This contribution proposes the use of evolutionary algorithms to select the most influential hyperrectangles to obtain accurate and simple models in classification tasks. The proposal is compared with the most representative nearest hyperrectangle learning approaches and the results obtained show that the evolutionary proposal outperforms them in accuracy and requires storing a lower number of hyperrectangles.

10.1109/isda.2009.238

Cite

Citations (12)

A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification

IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (2011)

Isaac Triguero Joaquín Derrac Salvador García Francisco Herrera

The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve classification and pattern recognition tasks. Despite its high classification accuracy, this rule suffers from several shortcomings in time response, noise sensitivity, and high storage requirements. These weaknesses have been tackled by many different approaches, including a good and well-known solution that we can find in the literature, which consists of the reduction of the data used for the classification rule (training data). Prototype reduction techniques can be divided into two different approaches, which are known as prototype selection and prototype generation (PG) or abstraction. The former process consists of choosing a subset of the original training data, whereas PG builds new artificial prototypes to increase the accuracy of the NN classification. In this paper, we provide a survey of PG methods specifically designed for the NN rule. From a theoretical point of view, we propose a taxonomy based on the main characteristics presented in them. Furthermore, from an empirical point of view, we conduct a wide experimental study that involves small and large datasets to measure their performance in terms of accuracy and reduction capabilities. The results are contrasted through nonparametrical statistical tests. Several remarks are made to understand which PG models are appropriate for application to different datasets.

Abstraction

Classification rule

10.1109/tsmcc.2010.2103939

Cite

Citations (260)

Actas de la XVII Conferencia de la Asociación Española para la Inteligencia Artificial

Óscar Luaces Francisco Herrera José A. Gámez Luis Martı́nez Edurne Barrenechea

Source

Cite

Citations (0)

Enabling Smart Data: Noise filtering in Big Data classification

arXiv (Cornell University) (2017)

Diego García‐Gil Julián Luengo Salvador García Francisco Herrera

In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem.

Data pre-processing

Noisy data

Data cleansing

Source

Cite

Citations (5)

A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges

Neurocomputing (2020)

Juan Luis Suárez Salvador García Francisco Herrera

Kernelization

Distance matrix

Similarity (geometry)

10.1016/j.neucom.2020.08.017

Cite

Citations (94)

Issue Information

International Journal of Intelligent Systems (2019)

Piotr Hon Diego García‐Gil Francisco Luque Sánchez Julián Luengo Salvador García

Binary classifi cation rule

10.1002/int.22024

Cite

Citations (0)

Distributed Entropy Minimization Discretizer for Big Data Analysis under Apache Spark

2015 IEEE Trustcom/BigDataSE/ISPA (2015)

Sergio Ramírez‐Gallego Salvador García Héctor Mouriño‐Talín David Martínez‐Rego

The astonishing rate of data generation on the Internet nowadays has caused that many classical knowledge extraction techniques have become obsolete. Data reduction techniques are required in order to reduce the complexity order held by these techniques. Among reduction techniques, discretization is one of the most important tasks in data mining process, aimed at simplifying and reducing continuous-valued data in large datasets. In spite of the great interest in this reduction mechanism, only a few simple discretization techniques have been implemented in the literature for Big Data. Thereby we propose a distributed implementation of the entropy minimization discretizer proposed by Fayyad and Irani using Apache Spark platform. Our solution goes beyond a simple parallelization, transforming the iterativity yielded by the original proposal in a single-step computation. Experimental results on two large-scale datasets show that our solution is able to improve the classification accuracy as well as boosting the underlying learning process.

Boosting

Minification

SPARK (programming language)

Data Reduction

Speedup

10.1109/trustcom.2015.559

Cite

Citations (22)

Imbalanced Classification for Big Data

Springer eBooks (2018)

Alberto Fernández Salvador García Mikel Galar Ronaldo C. Prati Bartosz Krawczyk

10.1007/978-3-319-98074-4_13