Navegando por Autor "Souza, Paulo Vitor de Campos"
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Item An advanced pruning method in the architecture of extreme learning machines using L1-regularization and bootstrapping.(2020) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Silva, Gustavo Rodrigues Lacerda; Braga, Antônio de Pádua; Lughofer, EdwinExtreme learning machines (ELMs) are efficient for classification, regression, and time series prediction, as well as being a clear solution to backpropagation structures to determine values in intermediate layers of the learning model. One of the problems that an ELM may face is due to a large number of neurons in the hidden layer, making the expert model a specific data set. With a large number of neurons in the hidden layer, overfitting is more likely and thus unnecessary information can deterioriate the performance of the neural network. To solve this problem, a pruning method is proposed, called Pruning ELM Using Bootstrapped Lasso BR-ELM, which is based on regularization and resampling techniques, to select the most representative neurons for the model response. This method is based on an ensembled variant of Lasso (achieved through bootstrap replications) and aims to shrink the output weight parameters of the neurons to 0 as many and as much as possible. According to a subset of candidate regressors having significant coefficient values (greater than 0), it is possible to select the best neurons in the hidden layer of the ELM. Finally, pattern classification tests and benchmark regression tests of complex real-world problems are performed by comparing the proposed approach to other pruning models for ELMs. It can be seen that statistically BR-ELM can outperform several related state-of-the-art methods in terms of classification accuracies and model errors (while performing equally to Pruning-ELM P-ELM), and this with a significantly reduced number of finally selected neurons.Item Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function.(2019) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Araújo, Vinicius Jonathan Silva; Rezende, Thiago SilvaThis paper proposes the use of fuzzification functions based on clustering of data based on their density to perform the granularization of the input space. The neurons formed in this layer are built through the density centers obtained with the input data of the model. In the second layer, the nullneurons aggregate the generated neurons in the first layer and allow the creation of if/then fuzzy rules. Even in the second layer, a regularization function is activated to determine the essential nullneurons. The concepts of extreme learning machine generate the weights used in the third layer, but with a regularizing factor. Finally, in the third layer, represented by an artificial neural network, it has a single neuron that the activation function uses robust functions to carry out the model. To verify the new training approach for fuzzy neural networks, we performed real and synthetic database tests for the pattern classification, which led to the conclusion that the data density-based approach the use of regularization factors in the second model layer and neurons with more robust activation functions allowed better results compared to other classifiers that use the concepts of extreme learning machine.Item Extreme wavelet fast learning machine for evaluation of the default profle on financial transactions.(2020) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos BambirraExtreme learning machines enable multilayered neural networks to perform activities to facilitate the process and business dynamics. It acts in pattern classifcation, linear regression problems, and time series prediction. The fnancial area needs efcient models that can perform businesses in a short time. Credit card fraud and debits occur regularly, and efective decision making can avoid signifcant obstacles for both clients and fnancial companies. This paper proposes a training model for multilayer networks where the weights of the training algorithm are defned by the nature and characteristics of the dataset using the concepts of the wavelet transform. The traditional algorithm of weights’ defnition of the output layer is changed to a regularized method that acts more quickly in the description of the weights of the output layer. Finally, several activation functions are applied to the model to verify its efciency in several scenarios. This model was subjected to an extensive dataset and comparing to diferent machine learning approaches. Its answers were satisfactory in a short-time execution, proving that the Extreme Learning Machine works effciently to identify possible profles of defaulters in payments in the fnancial relationships involving a credit card.Item Pulsar detection for wavelets soda and regularized fuzzy neural networks based on andneuron and robust activation function.(2019) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Júnio; Araújo, Vanessa SouzaThe use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.