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10 results for “Silvia Nassar”
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Research ArticleOpen accessIn the author namesFuzzy Method for Online Learning of Bayesian Network ParametersMariana D.C. Lima, Silvia M. NassarIn learning problems, there are situations where training data is not fully available at the learning time. They are incrementally generated by time, defining a type of domain called online that has among its characteristics the possibility of data failure or even missing data. In Bayesian networks, learning is divided into two categories: structure (related to the graph of conditional … …Journal of Computer Science, Vol. 15, Issue 3 · 16 Mar 2019 · 7,498 accessesView PDFCite
Research ArticleOpen accessIn the author namesMCMC-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 PDFCite
Research ArticleOpen accessIn the author namesImpact 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 PDFCite
Research ArticleOpen accessIn the author namesFuzzy Modeling for Multi-Label Text Classification Supported by Classification AlgorithmsBeatriz Wilges, Gustavo Mateus, Silvia Nassar et al.The ever-increasing amount of information on the Web is organized in structured, semi-structured and unstructured data. Text classification systems, capable of handling such different structures, may facilitate the work of important tasks such as indexation and information retrieval in search engines. The objective of this research is to develop a method for the classification of documents into multiple categories with … …Journal of Computer Science, Vol. 12, Issue 7 · 22 Aug 2016 · 7,215 accessesView PDFCite
Review ArticleOpen accessIn the author namesAutomatic 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 PDFCite
Research ArticleOpen accessIn the author namesHEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKSMariana D.C. Lima, Silvia M. Nassar, Pedro Ivo R.B.G. Rodrigues et al.Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In … …Journal of Computer Science, Vol. 10, Issue 5 · 18 Jan 2014 · 7,054 accessesView PDFCite
Research ArticleOpen accessIn the author namesANALYSIS 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 PDFCite