Search Results - "de Souza, Renata M C R"

Regression applied to symbolic interval-spatial data by Freitas, Wanessa W. L., de Souza, Renata M. C. R., Amaral, Getúlio J. A., de Moraes, Ronei M.

“…Symbolic data analysis is a research area related to machine learning and statistics, which provides tools to describe geo-objects, and enables several types…”
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Interval regression model adequacy checking and its application to estimate school dropout in Brazilian municipality educational scenario by do Nascimento, Rafaella L. S., Fagundes, Roberta A. de A., de Souza, Renata M. C. R., Cysneiros, Francisco José A.

“…Interval-valued data have been commonly encountered in practice, and Symbolic Data Analysis provides a solution to the statistical treatment of these data…”
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Logistic regression-based pattern classifiers for symbolic interval data by de Souza, Renata M. C. R., Queiroz, Diego C. F., Cysneiros, Francisco José A.

“…This paper introduces different pattern classifiers for interval data based on the logistic regression methodology. Four approaches are considered. These…”
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Classifying breast lesions in Brazilian thermographic images using convolutional neural networks by Brasileiro, Flávia R. S., Sampaio Neto, Delmiro D., Silva Filho, Telmo M., Souza, Renata M. C. R. de, Araújo, Marcus C. de

“…Breast cancer is the leading cause of death from malignant tumors in women worldwide. Early diagnosis is essential for the treatment and cure of patients…”
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Statistical learning from Brazilian fake news by Lima, Gabriel B., Chaves, Thiago de M., Freitas, Wanessa W. L., Souza, Renata M. C. R.

“…Fake news is information that does not represent reality but is commonly shared on the internet as if it were true, mainly because of its dramatic, appealing,…”
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Interval symbolic feature extraction for thermography breast cancer detection by Araújo, Marcus C., Lima, Rita C.F., de Souza, Renata M.C.R.

“…•Malignant, benignant and cyst classes are classified using a symbolic feature extraction.•Features based on interval dissimilarities are obtained from breast…”
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Statistical Learning for Predicting School Dropout in Elementary Education: A Comparative Study by do Nascimento, Rafaella L. S., Fagundes, Roberta A. de A., de Souza, Renata M. C. R.

“…School dropout is a significant challenge for the education system. This phenomenon is present in different environments, modalities, and stages of education…”
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A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data by Pimentel, Bruno Almeida, de Souza, Renata M.C.R.

“…•A weighted multivariate fuzzy clustering method for interval-valued data is introduced.•Weights measure importance of each variable to compute membership…”
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Classifying metrics for assessing Object-Oriented Software Maintainability: A family of metrics’ catalogs by Saraiva, Juliana de A.G., de França, Micael S., Soares, Sérgio C.B., Filho, Fernando J.C.L., de Souza, Renata M.C.R.

“…•OOSM metrics’ categorization was proposed: 7 categories and 17 subcategories.•The categories were used as engine of the generation of OOSM metrics catalogs.•A…”
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Multivariate fuzzy k-modes algorithm by Maciel, Diêgo B. M., Amaral, Getulio J. A., de Souza, Renata M. C. R., Pimentel, Bruno A.

“…In the fuzzy k -modes clustering, there is just one membership degree of interest by class for each individual which cannot be sufficient to model ambiguity of…”
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Interval kernel regression by Fagundes, Roberta A.A., de Souza, Renata M.C.R., Cysneiros, Francisco José A.

“…Kernel regression is more attractive when it is not possible to determine explicit parametric form of the model and moreover, it does not depend on…”
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Clustering interval data through kernel-induced feature space by da Costa, Anderson F. B. F., Pimentel, Bruno A., de Souza, Renata M. C. R.

“…Recently, kernel-based clustering in feature space has shown to perform better than conventional clustering methods in unsupervised classification. In this…”
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Unsupervised pattern recognition models for mixed feature-type symbolic data by de Carvalho, Francisco de A.T., de Souza, Renata M.C.R.

“…Unsupervised pattern recognition methods for mixed feature-type symbolic data based on dynamical clustering methodology with adaptive distances are presented…”
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Fuzzy Kohonen clustering networks for interval data by de Almeida, Carlos W.D., de Souza, Renata M.C.R., Candeias, Ana L.B.

“…The Fuzzy Kohonen Clustering Network combines the idea of fuzzy membership values for learning rates. It is a kind of self-organizing fuzzy neural network that…”
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Exploratory spatial analysis for interval data: A new autocorrelation index with COVID-19 and rent price applications by Freitas, Wanessa W.L., de Souza, Renata M.C.R., Amaral, Getúlio J.A., De Bastiani, Fernanda

“…This paper aims to identify the behavior of interval data associated to its respective geospatial information with in the framework of Symbolic Data Analysis…”
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Parametrized linear regression for boxplot-multivalued data applied to the Brazilian Electric Sector by Reyes, Dailys M.A., Souza, Leandro C., de Souza, Renata M.C.R., de Oliveira, Adriano L.I.

“…Symbolic boxplot data can be considered as a particular case of the numerical multi-valued variable. This kind of symbolic data is an useful exploratory tool…”
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Multivariate Fuzzy C-Means algorithms with weighting by Pimentel, Bruno Almeida, de Souza, Renata M.C.R.

“…With the growing interest in automatic understanding, processing and summarization of data, several application domains, such as pattern recognition, machine…”
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Generalized linear models for symbolic polygonal data by do Nascimento, Rafaella L.S., de Souza, Renata M.C.R., de A. Cysneiros, Francisco José

“…Symbolic data analysis data has provided several advances in regression models concerning the type of symbolic variable. Due to the advantages of using…”
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A three-stage approach for modeling multiple time series applied to symbolic quartile data by Reyes, Dailys M.A., de Souza, Renata M.C.R., de Oliveira, Adriano L.I.

“…Data science concerns to look for ways for extracting knowledge from extraordinary volume of data in any domain. This paper introduces a new three-stage…”
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A multivariate fuzzy c-means method by Pimentel, Bruno A., de Souza, Renata M.C.R.

“…[Display omitted] ► Fuzzy c-means algorithm has shown good performance in detecting clusters. ► In this paper the algorithm produces a membership matrix for…”
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