Author

Silvia Modesto Nassar

Federal University of Santa Catarina, Brazil

4 articles in our journals

Articles where this person is an author, newest first. People are matched by their name as printed (no ORCID iD is recorded for them), not by name alone; search the name as text to find namesakes too.

Research ArticleOpen accessMCMC-Fuzzy: A Fuzzy Metric Applied to Bayesian Network Structure LearningBeatriz Wilges, Ademar Crotti Junior, Silvia Modesto NassarBayesian network structure learning is considered a complex task as the number of possible structures grows exponentially with the number of variables. Two main methods are used for Bayesian network structure learning: Conditional independence, a method in which a structure is created consistently with independence tests performed on data; and the heuristic search method that explores the structure space. Hybrid … …Journal of Computer Science, Vol. 14, Issue 8 · 24 Aug 2018 · 8,243 accessesView PDF Cite

Research ArticleOpen accessImpact of an Extra Layer on the Stacking Algorithm for Classification ProblemsRodolfo Lorbieski, Silvia Modesto NassarClassifying and making decisions are tasks performed by any human being in their daily lives. Learning algorithms have been widely studied as tools to aid information management, with an objective to maximize the generalization capacity. Learning algorithms can be used individually or as a committee of machines (ensembles). An ensemble uses the solutions provided by several machines, making different combinations … …Journal of Computer Science, Vol. 14, Issue 5 · 10 Jan 2018 · 8,286 accessesView PDF Cite

Review ArticleOpen accessAutomatic Methods for Generation of Type-1 and Interval Type-2 Fuzzy Membership FunctionsAndréia Alves dos Santos Schwaab, Silvia Modesto Nassar, Paulo José de Freitas FilhoGeneration of membership functions is an important step in construction of fuzzy systems. Since membership functions reflect what is known about the variables involved in a problem, when they are correctly modeled the system will behave in the manner that is expected in the context of the problem being addressed. Since their creation, type-1 membership functions have been used in … …Journal of Computer Science, Vol. 11, Issue 9 · 20 Dec 2015 · 7,289 accessesView PDF Cite

Research ArticleOpen accessANALYSIS OF BAYESIAN CLASSIFIER ACCURACYFelipe Schneider Costa, Maria Marlene De Souza Pires, Silvia Modesto NassarThe naïve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naïve) assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables), the … …Journal of Computer Science, Vol. 9, Issue 11 · 26 Sep 2013 · 8,375 accessesView PDF Cite