JStatCom

JStatCom is a software framework that makes it easy to integrate numerical procedures written in specialized programming languages, like Matlab, Gauss or Ox, with the Java world. Furthermore, it helps building Graphical User Interfaces (GUI) for mathematical procedures by providing sophisticated data management features that seamlessy interact with Java Swing components.


References in zbMATH (referenced in 53 articles )

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  1. Fernández, Alberto; Elkano, Mikel; Galar, Mikel; Sanz, José Antonio; Alshomrani, Saleh; Bustince, Humberto; Herrera, Francisco: Enhancing evolutionary fuzzy systems for multi-class problems: distance-based relative competence weighting with truncated confidences (DRCW-TC) (2016)
  2. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  3. Manukyan, Artür; Ceyhan, Elvan: Classification of imbalanced data with a geometric digraph family (2016)
  4. Ougiaroglou, Stefanos; Evangelidis, Georgios: Efficient editing and data abstraction by finding homogeneous clusters (2016)
  5. van den Burg, Gerrit J.J.; Groenen, Patrick J.F.: GenSVM: a generalized multiclass support vector machine (2016)
  6. García, David; Gámez, Juan Carlos; González, Antonio; Pérez, Raúl: An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach (2015)
  7. González, Sergio; Herrera, Francisco; García, Salvador: Monotonic random forest with an ensemble pruning mechanism based on the degree of monotonicity (2015) ioport
  8. Jackowski, Konrad: Adaptive splitting and selection algorithm for regression (2015) ioport
  9. López, Victoria; del Río, Sara; Benítez, José Manuel; Herrera, Francisco: Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data (2015) ioport
  10. Ralescu, Anca; Díaz, Irene; Rodríguez-Muñiz, Luis J.: A classification algorithm based on geometric and statistical information (2015)
  11. Reyes-Galaviz, Orion F.; Pedrycz, Witold: Granular fuzzy models: analysis, design, and evaluation (2015)
  12. Tomczak, Jakub M.; Ziȩba, Maciej: Probabilistic combination of classification rules and its application to medical diagnosis (2015)
  13. Acilar, Ayşe Merve; Arslan, Ahmet: A novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithm (2014) ioport
  14. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers (2014)
  15. Derrac, Joaquín; García, Salvador; Herrera, Francisco: Fuzzy nearest neighbor algorithms: taxonomy, experimental analysis and prospects (2014) ioport
  16. Elgibreen, Hebah; Aksoy, Mehmet: RULES-IT: incremental transfer learning with RULES family (2014) ioport
  17. Flores, M.Julia; Gámez, José A.; Martínez, Ana M.: Domains of competence of the semi-naive Bayesian network classifiers (2014)
  18. Gacto, M.J.; Galende, M.; Alcalá, R.; Herrera, F.: $\mathrmMETSK-HD^e$: a multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems (2014) ioport
  19. Galar, Mikel; Fernández, Alberto; Barrenechea, Edurne; Herrera, Francisco: Empowering difficult classes with a similarity-based aggregation in multi-class classification problems (2014)
  20. Johansson, Ulf; Boström, Henrik; Löfström, Tuve; Linusson, Henrik: Regression conformal prediction with random forests (2014)

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