Joao Paulo Papa | Universidade Estadual Paulista "Júlio de Mesquita Filho" (original) (raw)

Papers by Joao Paulo Papa

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 2019

During the image acquisition process, some level of noise is usually added to the data mainly due... more During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2018

A Deep Boltzmann Machine (DBM) is composed of a stack of learners called Restricted Boltzmann Mac... more A Deep Boltzmann Machine (DBM) is composed of a stack of learners called Restricted Boltzmann Machines (RBMs), which correspond to a specific kind of stochastic energy-based networks. In this work, a DBM is applied to a robust image denoising by minimizing the contribution of some of its top nodes, called "noise nodes", which often get excited when noise pixels are present in the given images. After training the DBM with noise and clean images, the detection and deactivation of the noise nodes allow reconstructing images with great quality, eliminating most of their noise. The results obtained from important public image datasets showed the validity of the proposed approach.

ArXiv, 2017

Optimization techniques play an important role in several scientific and real-world applications,... more Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.

Applied Soft Computing, 2020

Feature selection for a given model can be transformed into an optimization task. The essential i... more Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research.

Applied Soft Computing, 2017

Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.

Soft Computing, 2017

Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.

Information Sciences, 2015

Traditional machine learning algorithms very often assume statistically independent data samples.... more Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF-MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF-MRF. Experiments over high-and mediumresolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.

Computers & Electrical Engineering, 2016

The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been p... more The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010

Artificial intelligence techniques have been extensively used for the identification of several d... more Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011

Parkinson's disease (PD) automatic identification has been actively pursued over several work... more Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification.

2011 16th International Conference on Intelligent System Applications to Power Systems, 2011

Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N... more Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N. Souza, Member, IEEE, Rodrigo YM Nakamura, Jo˜ao P. Papa Abstract—Non-technical losses identification has been paramount in the last decade. ...

International Journal of Pattern Recognition and Artificial Intelligence, 2012

With several good research groups actively working in machine learning (ML) approaches, we have n... more With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of usi...

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2010 20th International Conference on Pattern Recognition, 2010

Abstract Traditional pattern recognition techniques can not handle the classification of large da... more Abstract Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for ...

Studies in Computational Intelligence, 2014

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2009 16th International Conference on Digital Signal Processing, 2009

Abstract This paper presents a novel, fast and accurate holistic method for face-recognition usin... more Abstract This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply principal component analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. ...

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

In this paper we deal with the problem of feature selection by introducing a new approach based o... more In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamenta... more Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is ...

2009 15th International Conference on Intelligent System Applications to Power Systems, 2009

Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued stud... more Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as artificial neural networks and support vector machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the optimum-path forest classifier for a fast non-technical losses recognition, which has been ...

Computers & Electrical Engineering, 2011

Finding an optimal subset of features that maximizes classification accuracy is still an open pro... more Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems.

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 2019

During the image acquisition process, some level of noise is usually added to the data mainly due... more During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2018

A Deep Boltzmann Machine (DBM) is composed of a stack of learners called Restricted Boltzmann Mac... more A Deep Boltzmann Machine (DBM) is composed of a stack of learners called Restricted Boltzmann Machines (RBMs), which correspond to a specific kind of stochastic energy-based networks. In this work, a DBM is applied to a robust image denoising by minimizing the contribution of some of its top nodes, called "noise nodes", which often get excited when noise pixels are present in the given images. After training the DBM with noise and clean images, the detection and deactivation of the noise nodes allow reconstructing images with great quality, eliminating most of their noise. The results obtained from important public image datasets showed the validity of the proposed approach.

ArXiv, 2017

Optimization techniques play an important role in several scientific and real-world applications,... more Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.

Applied Soft Computing, 2020

Feature selection for a given model can be transformed into an optimization task. The essential i... more Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research.

Applied Soft Computing, 2017

Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.

Soft Computing, 2017

Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.

Information Sciences, 2015

Traditional machine learning algorithms very often assume statistically independent data samples.... more Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF-MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF-MRF. Experiments over high-and mediumresolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.

Computers & Electrical Engineering, 2016

The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been p... more The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010

Artificial intelligence techniques have been extensively used for the identification of several d... more Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011

Parkinson's disease (PD) automatic identification has been actively pursued over several work... more Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification.

2011 16th International Conference on Intelligent System Applications to Power Systems, 2011

Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N... more Page 1. 1 Electrical Consumers Data Clustering Through Optimum-Path Forest Caio CO Ramos, André N. Souza, Member, IEEE, Rodrigo YM Nakamura, Jo˜ao P. Papa Abstract—Non-technical losses identification has been paramount in the last decade. ...

International Journal of Pattern Recognition and Artificial Intelligence, 2012

With several good research groups actively working in machine learning (ML) approaches, we have n... more With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of usi...

The user has requested enhancement of the downloaded file.

2010 20th International Conference on Pattern Recognition, 2010

Abstract Traditional pattern recognition techniques can not handle the classification of large da... more Abstract Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for ...

Studies in Computational Intelligence, 2014

The user has requested enhancement of the downloaded file.

2009 16th International Conference on Digital Signal Processing, 2009

Abstract This paper presents a novel, fast and accurate holistic method for face-recognition usin... more Abstract This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply principal component analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. ...

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

In this paper we deal with the problem of feature selection by introducing a new approach based o... more In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamenta... more Abstract The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is ...

2009 15th International Conference on Intelligent System Applications to Power Systems, 2009

Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued stud... more Abstract Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as artificial neural networks and support vector machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the optimum-path forest classifier for a fast non-technical losses recognition, which has been ...

Computers & Electrical Engineering, 2011

Finding an optimal subset of features that maximizes classification accuracy is still an open pro... more Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems.

A sample chapter of the Book on "Bio-inspired Computation and Applications in Image Processing" ... more A sample chapter of the Book on
"Bio-inspired Computation and Applications in Image Processing"
(Elsevier, 2016).