Publications
Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems,
, IEEE Transactions on Fuzzy Systems, Aug, Volume 15, Number 4, p.551-562, (2007)
A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers,
, 2008 3rd International Workshop on Genetic and Evolving Systems, March, p.11-16, (2008)
Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems,
, 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), March, p.75-80, (2010)
Comments on “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization” by Eyke Hüllermeier,
, International Journal of Approximate Reasoning, Volume 55, Number 7, p.1583 - 1587, (2014)
Training set selection for monotonic ordinal classification,
, Data & Knowledge Engineering, Volume 112, p.94 - 105, (2017)
predtoolsTS: R package for streamlining time series forecasting,
, Progress in Artificial Intelligence, 06/2019, Volume 8, p.505–510, (2019)
Charte2019_Article_PredtoolsTSRPackageForStreamli.pdf (390.07 KB)
Smartdata: Data preprocessing to achieve smart data in R,
, Neurocomputing, 09/2019, Volume 360, p.1-13, (2019)
A Comprehensive and Didactic Review on Multilabel Learning Software Tools,
, IEEE Access, 03/2020, Volume 8, p.50330-50354, (2020)
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications,
, Neurocomputing, Volume 410, p.237-270, (2020)
Decomposition-Fusion for Label Distribution Learning,
, Information Fusion, 02/2021, Volume 66, p.64-75, (2021)
1-s2.0-S1566253520303596-main.pdf (947.74 KB)
Synthetic Sample Generation for Label Distribution Learning,
, Information Sciences, 01/2021, Volume 544, p.197-213, (2021)
1-s2.0-S0020025520307544-main.pdf (1.36 MB)