J
Jacinto Mata
Researcher at University of Huelva
Publications - 32
Citations - 682
Jacinto Mata is an academic researcher from University of Huelva. The author has contributed to research in topics: Computer science & Association rule learning. The author has an hindex of 7, co-authored 27 publications receiving 570 citations.
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Journal ArticleDOI
Overview of BioCreative II gene mention recognition
Larry Smith,Lorraine K. Tanabe,Rie Johnson nee Ando,Cheng-Ju Kuo,I-Fang Chung,Chun-Nan Hsu,Yu-Shi Lin,Roman Klinger,Christoph M. Friedrich,Kuzman Ganchev,Manabu Torii,Hongfang Liu,Barry Haddow,Craig A. Struble,Richard J. Povinelli,Andreas Vlachos,William A. Baumgartner,Lawrence Hunter,Bob Carpenter,Richard Tzong-Han Tsai,Richard Tzong-Han Tsai,Hong-Jie Dai,Hong-Jie Dai,Feng Liu,Yifei Chen,Chengjie Sun,Sophia Katrenko,Pieter Adriaans,Christian Blaschke,Rafael Torres,Mariana Neves,Preslav Nakov,Preslav Nakov,Anna Divoli,Manuel Maña-López,Jacinto Mata,W. John Wilbur +36 more
TL;DR: It is demonstrated that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.
Proceedings ArticleDOI
An evolutionary algorithm to discover numeric association rules
TL;DR: This paper uses an evolutionary algorithm to find the intervals of each attribute that conforms a frequent itemset within the database and evaluates the tool with synthetic and real databases to check the efficiency of the algorithm.
Book ChapterDOI
Mining Numeric Association Rules with Genetic Algorithms
TL;DR: In this last decade, association rules are being, inside Data Mining techniques, one of the most used tools to find relationships among attributes of a database.
Journal ArticleDOI
Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation.
TL;DR: In this paper, a machine learning model was developed to predict the severity of infection and mortality from among clinical laboratory parameters, such as lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels.
Book ChapterDOI
A multi-objective evolutionary approach for subgroup discovery
TL;DR: A new evolutionary multi-objective algorithm (GARSD) for Subgroup Discovery tasks is presented that can work with both discrete and continuous attributes without the need for a previous discretization.