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Papers by Rewbenio A . Frota

Research paper thumbnail of On the classification of mental tasks: a performance comparison of neural and statistical approaches

Machine Learning for …, 2004

Electroencephalogram (EEG) signals represent an important class of biological signals whose behav... more Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior can be used to diagnose anomalies in brain activity. The goal of this paper is to find a concise representation of EEG data, corresponding to 5 mental tasks performed by different individuals, for classification purposes. For that, we propose the use of Welch's periodogram as a powerful feature extractor and compare the performance of SOMand MLP-based neural classifiers with that of standard Bayes optimal classifier. The results show that the Welch's periodogram allow all classifiers to achieve higher classification rates (73%-100%) than those presented so far in the literature (≤ 71%).

Research paper thumbnail of A unifying methodology for the evaluation of neural network models on novelty detection tasks

Pattern Analysis & Applications, 2012

An important issue in data analysis and pattern classification is the detection of anomalous obse... more An important issue in data analysis and pattern classification is the detection of anomalous observations and its influence on the classifier's performance. In this paper we introduce a novel methodology to systematically compare the performance of neural network (NN) methods applied to novelty detection problems. Initially, we describe the most common NN-based novelty detection techniques. Then, we generalize to the supervised case a recently proposed unsupervised novelty detection method for computing reliable decision thresholds. We illustrate how to use the proposed methodology to evaluate the performances of supervised and unsupervised NN-based novelty detectors on a real-world benchmarking dataset, assessing their sensitivity to training parameters, such as data scaling, number of neurons, training epochs and size of the training set.

Research paper thumbnail of An Evaluation of Neural Network Methods and Data Preparation Strategies for Novelty Detection

deti.ufc.br

An important issue in the design of a model for a particular data set is the quality of the data ... more An important issue in the design of a model for a particular data set is the quality of the data concerning the presence of anomalous observations (outliers) and their influence in the performance of pattern classifiers. Common approaches to deal with outliers remove them from data or improve the robustness of the machine learning method by handling outliers directly. We explore these two views by introducing a systematic methodology to compare the performance of neural methods applied to novelty detection. Firstly, we describe in a tutorial-like fashion the most common neural-based novelty detection techniques. Then, in order to compute reliable decision thresholds, we generalize the recent application of the bootstrap resampling technique to unsupervised novelty detection to the supervised case, and propose a outlier removal procedure based on it. Finally, we evaluate the performance of the neural network methods through simulations on a breast cancer data set, assessing their robustness to outliers and their sensitivity to training parameters, such as data scaling, number of neurons, training epochs and size of the training set. We conclude the paper by discussing the obtained results.

Research paper thumbnail of A New Approach to Fault Detection and Diagnosis in Cellular Systems Using Competitive Learning

Proceedings of the VII …, 2004

We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular netw... more We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal functioning of a CDMA2000 cellular system. After training is completed, a normality profile is constructed by means of the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compared the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods.

Research paper thumbnail of Anomaly detection in mobile communication networks using the self-organizing map

Journal of Intelligent and Fuzzy …, 2007

Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnorma... more Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. In this paper we propose a general procedure for the computation of decision thresholds for anomaly detection in mobile communication networks. The proposed method is based on Kohonen's Self-Organizing Map (SOM) and the computation of nonparametric (i.e. percentile-based) confidence intervals. Through simulations we compare the performance of the proposed and standard SOM-based anomaly detection methods with respect to the false positive rates produced.

Research paper thumbnail of PROPOSTA DE UMA METODOLOGIA NÃO-PARAMÉTRICA PARA AVALIAÇÃO DE REDES NEURAIS EM TAREFAS DE DETECÇÃO DE NOVIDADES

deti.ufc.br

Artificial neural networks have been successfully used in novelty detection applications. Several... more Artificial neural networks have been successfully used in novelty detection applications. Several approaches for almost every network architecture are available what makes it hard to know which neural-based method is the best for a given application or data configuration. Trying to give some introductory steps, we introduce a new systematic methodology to compare the performance of neural methods applied to novelty detection. In order to compute reliable decision thresholds, we generalize the recent application of the bootstrap resampling technique to unsupervised novelty detection to the supervised case. Finally, we evaluate the performance of the neural network methods through simulations on a breast cancer data set, assessing their robustness to outliers and their sensitivity to training parameters, such as number of neurons, training epochs and size of the training set.

Research paper thumbnail of Nonstationary Time Series Prediction Using Local Models Based on Competitive Neural Networks

Innovations in Applied Artificial …, 2004

In this paper, we propose a general approach for the application of competitive neural networks t... more In this paper, we propose a general approach for the application of competitive neural networks to nonstationary time series prediction. The underlying idea is to combine the simplicity of the standard least-squares (LS) parameter estimation technique with the information compression power of unsupervised learning methods. The proposed technique builds the regression matrix and the prediction vector required by the LS method through the weight vectors of the K first winning neurons (i.e. those most similar to the current input vector). Since only few neurons are used to build the predictor for each input vector, this approach develops local representations of a nonstationary time series suitable for prediction tasks. Three competitive algorithms (WTA, FSCL and SOM) are tested and their performances compared with the conventional approach, confirming the efficacy of the proposed method.

Research paper thumbnail of Competitive Neural Networks for Fault Detection and Diagnosis in 3G Cellular Systems

… and Networking-ICT …, 2004

We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular netw... more We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal behavior of a CDMA2000 cellular system. After training, a normality profile is built from the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compare the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods.

Research paper thumbnail of On the classification of mental tasks: a performance comparison of neural and statistical approaches

Machine Learning for …, 2004

Electroencephalogram (EEG) signals represent an important class of biological signals whose behav... more Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior can be used to diagnose anomalies in brain activity. The goal of this paper is to find a concise representation of EEG data, corresponding to 5 mental tasks performed by different individuals, for classification purposes. For that, we propose the use of Welch's periodogram as a powerful feature extractor and compare the performance of SOMand MLP-based neural classifiers with that of standard Bayes optimal classifier. The results show that the Welch's periodogram allow all classifiers to achieve higher classification rates (73%-100%) than those presented so far in the literature (≤ 71%).

Research paper thumbnail of Condition monitoring of 3G cellular networks through competitive neural models

Neural Networks, …, 2005

We develop an unsupervised approach to condition monitoring of cellular networks using competitiv... more We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms (WTA, FSCL, SOM and Neural-Gas) and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods. and Wideband CDMA, respectively, which are 3G technologies capable of providing speeds of up to 2Mbits/sec. 2 CDMA2000, also called 1xRTT (single carrier radio transmission technology), is a 3G wireless technology based on the CDMA platform which has the capability of providing speeds of up to 144 Kbps.

Research paper thumbnail of On the classification of mental tasks: a performance comparison of neural and statistical approaches

Machine Learning for …, 2004

Electroencephalogram (EEG) signals represent an important class of biological signals whose behav... more Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior can be used to diagnose anomalies in brain activity. The goal of this paper is to find a concise representation of EEG data, corresponding to 5 mental tasks performed by different individuals, for classification purposes. For that, we propose the use of Welch's periodogram as a powerful feature extractor and compare the performance of SOMand MLP-based neural classifiers with that of standard Bayes optimal classifier. The results show that the Welch's periodogram allow all classifiers to achieve higher classification rates (73%-100%) than those presented so far in the literature (≤ 71%).

Research paper thumbnail of A unifying methodology for the evaluation of neural network models on novelty detection tasks

Pattern Analysis & Applications, 2012

An important issue in data analysis and pattern classification is the detection of anomalous obse... more An important issue in data analysis and pattern classification is the detection of anomalous observations and its influence on the classifier's performance. In this paper we introduce a novel methodology to systematically compare the performance of neural network (NN) methods applied to novelty detection problems. Initially, we describe the most common NN-based novelty detection techniques. Then, we generalize to the supervised case a recently proposed unsupervised novelty detection method for computing reliable decision thresholds. We illustrate how to use the proposed methodology to evaluate the performances of supervised and unsupervised NN-based novelty detectors on a real-world benchmarking dataset, assessing their sensitivity to training parameters, such as data scaling, number of neurons, training epochs and size of the training set.

Research paper thumbnail of An Evaluation of Neural Network Methods and Data Preparation Strategies for Novelty Detection

deti.ufc.br

An important issue in the design of a model for a particular data set is the quality of the data ... more An important issue in the design of a model for a particular data set is the quality of the data concerning the presence of anomalous observations (outliers) and their influence in the performance of pattern classifiers. Common approaches to deal with outliers remove them from data or improve the robustness of the machine learning method by handling outliers directly. We explore these two views by introducing a systematic methodology to compare the performance of neural methods applied to novelty detection. Firstly, we describe in a tutorial-like fashion the most common neural-based novelty detection techniques. Then, in order to compute reliable decision thresholds, we generalize the recent application of the bootstrap resampling technique to unsupervised novelty detection to the supervised case, and propose a outlier removal procedure based on it. Finally, we evaluate the performance of the neural network methods through simulations on a breast cancer data set, assessing their robustness to outliers and their sensitivity to training parameters, such as data scaling, number of neurons, training epochs and size of the training set. We conclude the paper by discussing the obtained results.

Research paper thumbnail of A New Approach to Fault Detection and Diagnosis in Cellular Systems Using Competitive Learning

Proceedings of the VII …, 2004

We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular netw... more We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal functioning of a CDMA2000 cellular system. After training is completed, a normality profile is constructed by means of the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compared the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods.

Research paper thumbnail of Anomaly detection in mobile communication networks using the self-organizing map

Journal of Intelligent and Fuzzy …, 2007

Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnorma... more Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. In this paper we propose a general procedure for the computation of decision thresholds for anomaly detection in mobile communication networks. The proposed method is based on Kohonen's Self-Organizing Map (SOM) and the computation of nonparametric (i.e. percentile-based) confidence intervals. Through simulations we compare the performance of the proposed and standard SOM-based anomaly detection methods with respect to the false positive rates produced.

Research paper thumbnail of PROPOSTA DE UMA METODOLOGIA NÃO-PARAMÉTRICA PARA AVALIAÇÃO DE REDES NEURAIS EM TAREFAS DE DETECÇÃO DE NOVIDADES

deti.ufc.br

Artificial neural networks have been successfully used in novelty detection applications. Several... more Artificial neural networks have been successfully used in novelty detection applications. Several approaches for almost every network architecture are available what makes it hard to know which neural-based method is the best for a given application or data configuration. Trying to give some introductory steps, we introduce a new systematic methodology to compare the performance of neural methods applied to novelty detection. In order to compute reliable decision thresholds, we generalize the recent application of the bootstrap resampling technique to unsupervised novelty detection to the supervised case. Finally, we evaluate the performance of the neural network methods through simulations on a breast cancer data set, assessing their robustness to outliers and their sensitivity to training parameters, such as number of neurons, training epochs and size of the training set.

Research paper thumbnail of Nonstationary Time Series Prediction Using Local Models Based on Competitive Neural Networks

Innovations in Applied Artificial …, 2004

In this paper, we propose a general approach for the application of competitive neural networks t... more In this paper, we propose a general approach for the application of competitive neural networks to nonstationary time series prediction. The underlying idea is to combine the simplicity of the standard least-squares (LS) parameter estimation technique with the information compression power of unsupervised learning methods. The proposed technique builds the regression matrix and the prediction vector required by the LS method through the weight vectors of the K first winning neurons (i.e. those most similar to the current input vector). Since only few neurons are used to build the predictor for each input vector, this approach develops local representations of a nonstationary time series suitable for prediction tasks. Three competitive algorithms (WTA, FSCL and SOM) are tested and their performances compared with the conventional approach, confirming the efficacy of the proposed method.

Research paper thumbnail of Competitive Neural Networks for Fault Detection and Diagnosis in 3G Cellular Systems

… and Networking-ICT …, 2004

We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular netw... more We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal behavior of a CDMA2000 cellular system. After training, a normality profile is built from the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compare the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods.

Research paper thumbnail of On the classification of mental tasks: a performance comparison of neural and statistical approaches

Machine Learning for …, 2004

Electroencephalogram (EEG) signals represent an important class of biological signals whose behav... more Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior can be used to diagnose anomalies in brain activity. The goal of this paper is to find a concise representation of EEG data, corresponding to 5 mental tasks performed by different individuals, for classification purposes. For that, we propose the use of Welch's periodogram as a powerful feature extractor and compare the performance of SOMand MLP-based neural classifiers with that of standard Bayes optimal classifier. The results show that the Welch's periodogram allow all classifiers to achieve higher classification rates (73%-100%) than those presented so far in the literature (≤ 71%).

Research paper thumbnail of Condition monitoring of 3G cellular networks through competitive neural models

Neural Networks, …, 2005

We develop an unsupervised approach to condition monitoring of cellular networks using competitiv... more We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms (WTA, FSCL, SOM and Neural-Gas) and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods. and Wideband CDMA, respectively, which are 3G technologies capable of providing speeds of up to 2Mbits/sec. 2 CDMA2000, also called 1xRTT (single carrier radio transmission technology), is a 3G wireless technology based on the CDMA platform which has the capability of providing speeds of up to 144 Kbps.