S. García

Publications
Influence

KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

J. Alcalá-Fdez, A. Fernández, J. Luengo, J. Derrac, S. García
Computer Science
J. Multiple Valued Log. Soft Comput.
2011

The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformatandshowssomeresultsofalgorithmsinthesedatasets; some guidelines for including new algorithms in KEEL, helping the researcherstomaketheirmethodseasilyaccessibletootherauthorsandtocompare theresults of many approaches already included within the KEEL software. Expand

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Managing by Values: A Corporate Guide to Living, Being Alive, and Making a Living in the 21st Century

S. Dolan, S. García, B. Richley
Engineering
28 July 2006

Preface Introduction PART I: MANAGEMENT BY VALUES: LOGIC AND CONTENT Managing by Values (MBV): Its Foundation and Evolution Values: But, what Actually are they? Renew or Die: The Importance of… Expand

Big data preprocessing: methods and prospects

S. García, S. Ramírez-Gallego, J. Luengo, J. Benítez, F. Herrera
Computer Science
1 November 2016

The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Expand

Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce

S. Ramírez-Gallego, A. Fernández, S. García, M. Chen, Francisco Herrera
Computer Science
Inf. Fusion
1 July 2018

We enumerate and analyze two alternative methodologies that may be found both in the specialized literature and in standard Machine Learning libraries for Big Data. Expand

Multivariate Discretization Based on Evolutionary Cut Points Selection for Classification

S. Ramírez-Gallego, S. García, J. Benítez, F. Herrera
Computer Science, Medicine
IEEE Transactions on Cybernetics
1 March 2016

In this paper, we propose the use of evolutionary algorithms to select a subset of cut points that defines the best possible discretization scheme of a data set using a wrapper fitness function. Expand

A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal

M. Galar, J. Derrac, +9 authors Francisco Herrera
Computer Science
Knowl. Based Syst.
1 June 2015

We reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Expand

Diagnose Effective Evolutionary Prototype Selection Using an Overlapping Measure

S. García, J. Cano, Ester Bernadó-Mansilla, Francisco Herrera
Computer Science
Int. J. Pattern Recognit. Artif. Intell.
1 December 2009

In this paper, we analyze the behavior of the evolutionary prototype selection strategy, considering a complexity measure for classification problems based on overlapping. Expand

A distributed evolutionary multivariate discretizer for Big Data processing on Apache Spark

S. Ramírez-Gallego, S. García, J. Benítez, Francisco Herrera
Computer Science
Swarm Evol. Comput.
1 February 2018

This paper proposes a distributed discretization algorithm for Big Data analytics based on evolutionary optimization. Expand

Data discretization: taxonomy and big data challenge

S. Ramírez-Gallego, S. García, +5 authors Francisco Herrera
Computer Science
Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
2016

Discretization of numerical data is one of the most influential data preprocessing tasks in knowledge discovery and data mining. Expand

A comparison on scalability for batch big data processing on Apache Spark and Apache Flink

D. García-Gil, S. Ramírez-Gallego, S. García, F. Herrera
Computer Science
1 March 2017

We perform a comparative study for batch data processing of the scalability of two popular frameworks for processing and storing Big Data, Apache Spark and Apache Flink. Expand

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