Papers by Anne Magaly de Paula Canuto
This study focuses on the hyperspectral image from the University of Pavia, performing various ma... more This study focuses on the hyperspectral image from the University of Pavia, performing various manipulations to derive new datasets and observe their impact on the classification results. The aim is to automate the pixel classification process using machine learning algorithms with different training and testing splits. Additionally, ensemble classifiers were implemented to improve accuracy. The results show that the Multilayer Perceptron (MLP) achieved the highest accuracy among the implemented methods, surpassing 85% and providing similar results to the ensemble classifiers. The original dataset (untouched) and the dataset reduced to 20 principal components using Principal Component Analysis (PCA) yielded the best results. It is worth noting that considering unlabeled pixels limited the accuracy of the implemented algorithms.
biométricas, tendo publicado mais de 150 artigos científicos em congressos e periódicos. Sua pesq... more biométricas, tendo publicado mais de 150 artigos científicos em congressos e periódicos. Sua pesquisa tem se focado na melhoria das estruturas de classificação existentes, visando melhorar seu desempenho na tarefa de classificação, incluindo a autenticação de usuários utilizando dados biométricos.
A Class-Based Feature Selection Method for Ensemble Systems
2008 Eighth International Conference on Hybrid Intelligent Systems, 2008
Page 1. A Class-Based Feature Selection Method for Ensemble Systems Karliane MO Vale, Filipe G Di... more Page 1. A Class-Based Feature Selection Method for Ensemble Systems Karliane MO Vale, Filipe G Dias, Anne MP Canuto and Marcílio CP Souto Informatics and Applied Mathematics Department, Federal University of RN ...
Research, Society and Development
ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental disorder characterized by... more ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental disorder characterized by harmful levels of inattention, disorganization, and/or hyperactivity-impulsivity. In childhood, these symptoms often overlap with those of other disorders, and they tend to persist into adulthood, interfering with relationships and academic and work life. Diagnosis, traditionally made by assessing the patient, i.e., testing and listening to relatives and teachers, has already been aided by neuroimaging. However, the visual analysis of such images to make a psychiatric diagnosis is a complex and sometimes time-consuming task. For this reason, computer-aided diagnostic tools have increasingly evolved that, when combined with machine learning (ML) techniques, can accelerate, facilitate, and maximize the accuracy of diagnoses. Nevertheless, research evaluating ML models for classifying ADHD considering severity using images of the brain SPECT (Single Photon Emission Computed Tomography) is ...
IEEE Access
Semi-supervised learning (SSL) is a machine learning approach that integrates supervised and unsu... more Semi-supervised learning (SSL) is a machine learning approach that integrates supervised and unsupervised learning mechanisms. This integration may be done in different ways and one possibility is to use a wrapper-based strategy. The main aim of a wrapper-based strategy is to use a small number of labelled instances to create a learning model. Then, this created model is used in a labelling process, where some unlabelled instances are labelled, and consequently, these instances are incorporated into the labelled set. One important aspect of a wrapper-based SSL method is the selection of unlabelled instances to be labelled in the labelling process. In other words, an efficient selection process plays an important role in the design of a wrapper-based SSL method since it can lead to an efficient labelling process, and in turn, the creation of efficient learning models. In this paper, we propose the use of three selection methods that can be applied to wrapper-based SSL methods. The main idea is to use two different selection criteria, prediction confidence or classification agreement with a distance metric, to perform an efficient selection of the unlabelled instances. In order to assess the feasibility of the proposed approach, the selection methods are applied in two wellknown wrapper-based SSL methods, which are: Self-training and Co-training. Additionally, an empirical analysis will be conducted in which we compare the standard Self-training and Co-training methods against the proposed versions of these two SSL methods over 35 classification datasets. INDEX TERMS Artificial intelligence, machine learning, semi-supervised learning, self-training semisupervised method, co-training semi-supervised method.
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
Object Detection (OD) is an important task in Computer Vision with many practical applications. F... more Object Detection (OD) is an important task in Computer Vision with many practical applications. For some use cases, OD must be done on videos, where the object of interest has a periodic motion. In this paper, we formalize the problem of periodic OD, which consists in improving the performance of an OD model in the specific case where the object of interest is repeating similar spatio-temporal trajectories with respect to the video frames. The proposed approach is based on training a Gaussian Process to model the periodic motion, and use it to filter out the erroneous predictions of the OD model. By simulating various OD models and periodic trajectories, we demonstrate that this filtering approach, which is entirely data-driven, improves the detection performance by a large margin.
Investigação Científica nas Ciências Sociais Aplicadas 2, Dec 23, 2019
Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atrib... more Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição 4.0 Internacional (CC BY 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.
Towards a Methodology for Developing Agent-Based Simulations: The MASim Methodology
Autonomous Agents & Multiagent Systems/International Conference on Autonomous Agents, 2004
This paper presents the general aspects of the MASim methodology, aimed for the development of ag... more This paper presents the general aspects of the MASim methodology, aimed for the development of agent-based simulations. MASim employs features common to the development of agent-based software as well as to the development of simulation models. It also borrows concepts used in mainstream of the software engineering process.
2020 International Joint Conference on Neural Networks (IJCNN), 2020
Semi-supervised learning (SSL) is a paradigm that has been continuously used in data classificati... more Semi-supervised learning (SSL) is a paradigm that has been continuously used in data classification tasks in datasets that do not have enough labeled instances to train a supervised model with a minimum acceptable accuracy. In this context, data stream classification in dynamic environments appears as a natural application for this approach, because changes in data distribution contribute to decrease the performance of the classification algorithms. In this paper, we have proposed a framework, refered to as Dynamic Data Stream Learning (DyDaSL), that implements an auto-adaptive classifier ensemble-which is able to evaluate and replace classifiers with decreasing performance. This platform uses the FlexCon-C method, which is a variant of the self-training SSL algorithm that adjusts a confidence threshold dynamically, in each iteration, to define which instances will be labeled. Experimental tests on synthetic and real datasets show that the proposed approach obtains better results than traditional approaches using four evaluation metrics: accuracy, F-score, precision, and recall.
IEEE Access, 2021
Semi-supervised learning is a machine learning approach that integrates supervised and unsupervis... more Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-supervised learning is attractive due to its potential to use labeled and unlabeled data to perform better than supervised learning. This paper consists of a study in the field of semi-supervised learning and implements changes on two well-known semisupervised learning algorithms: self-training and co-training. In the literature, it is common to develop researches that change the structure of these algorithms, however, none of them proposes automating the labeling process of unlabeled instances, which is the main purpose of this work. In order to achieve this goal, three methods are proposed: FlexCon-G, FlexCon and FlexCon-C. The main difference among these methods is the way in which the confidence rate is calculated and the strategy used to select a label in each iteration. In order to evaluate the proposed methods' performance, an empirical analysis is conducted, in which the performance of these methods has been evaluated on 30 datasets with different characteristics. The obtained results indicate that all three proposed methods perform better than the original self-training and co-training methods, in most analysed cases.
IEEE Access, 2021
Machine Learning (ML) is a field that aims to develop efficient techniques to provide intelligent... more Machine Learning (ML) is a field that aims to develop efficient techniques to provide intelligent decision making solutions to complex real problems. Among the different ML structures, a classifier ensemble has been successfully applied to several classification domains. A classifier ensemble is composed of a set of classifiers (specialists) organized in a parallel way, and it is able to produce a combined decision for an input pattern (instance). Although Classifier ensembles have proved to be robust in several applications, an important issue is always brought to attention is the ensemble's structure. In other words, the correction definition of its structure, like the number and type of classifiers and the aggregation method, has an important role in its performance. Usually, an exhaustive testing and evaluation process is required to better define the ideal structure for an ensemble. Aiming to produce an interesting investigation in this field, this paper proposes two new approaches for automatic recommendation of classifier ensemble structure, using meta-learning to recommend three of these important parameters: type of classifier, number of base classifiers, and the aggregation method. The main aim is to provide a robust structure in a simple and fast way. In this analysis, five well known classification algorithms will be used as base classifiers of the ensemble: kNN (Nearest Neighbors), DT (Decision Tree), RF (Random Forest), NB (Naive Bayes) e LR (Logistic Regression). Additionally, the classifier ensembles will be evaluated using seven different strategies as aggregation functions: HV (Hard Voting), SV (Soft Voting), LR (Logistic Regression), SVM (Support Vector Machine), NB(Naive Bayes), MLP (Multilayer perceptron) e DT (Decision Tree). The empirical analysis shows that our approach can lead to robust classifier ensembles, for the majority of the analysed cases. INDEX TERMS Classifier ensembles, meta-learning, multiple classifier system, machine learning.
Regression Ensembles for Fast Design Space Exploration of Heterogeneous Hardware Designs
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
In architectural design of embedded systems, machine learning (ML) has become a promising solutio... more In architectural design of embedded systems, machine learning (ML) has become a promising solution to provide robustness to the design space exploration (DSE) of large hardware designs. However, given the large diversity of embedded applications, a main challenge in the design of a high-accuracy predictor is to select one ML algorithm to encompass a wide range of applications. In this context, regression ensemble is a promising solution since it can use multiple models and combine their predictions. In this work we employ the use of ensemble methods to predict performance when running different applications in different heterogeneous designs composed of a general purpose processor (GPPs) and a reconfigurable accelerator (RA). In our investigation, we evaluate three ensemble methods, Random Forest, AdaBoosting and Gradient Boosting. So, we compare them to the most used regression algorithms found in literature to perform DSE of computer architectures. Results show an error prediction...
Using Meta-learning in the Selection of the Combination Method of a Classifier Ensemble
2018 International Joint Conference on Neural Networks (IJCNN), 2018
Classifier ensembles have been widely studied in the literature as an attempt to increase the per... more Classifier ensembles have been widely studied in the literature as an attempt to increase the performance of individual classification structures. An important issue in the design of classifier ensemble is the definition of its structure. More specifically, the selection of the best individual classifiers and the combination method for an ensemble. Usually, an exhaustive test-and-trial process may be needed to define its structure. In parallel, new contributions of meta-learning have been presented as an efficient alternative to the automatic recommendation of classification algorithms. In this paper, we will apply meta-learning in the process of recommendation of the combination method of ensemble systems. The main goal of this paper is to provide one step towards the automatic design of classifier ensembles. In order to achieve this goal, three different representation approaches for the meta-learning recommendation process are proposed. In addition, an empirical analysis is perfo...
Dynamic Feature Selection for Classifier Ensembles
In this paper, we propose a novel approach for dynamic feature selection to be applied in classif... more In this paper, we propose a novel approach for dynamic feature selection to be applied in classifier ensembles. This method selects the best attribute subsets for an individual instance or a group of instances of an input dataset. Hence, each testing instance is classified using a unique feature subset in the classification process. The main aim of this paper is to extend a dynamic feature selection method that was proposed for single classifiers, adjusting this approach to be used in classifier ensembles. In order to validate our proposed method, an empirical analysis is conducted to investigate the effectiveness of approach compared to existing ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed method to the existing ensemble methods.
2019 8th Brazilian Conference on Intelligent Systems (BRACIS), Oct 1, 2019
The use of soft biometrics as an auxiliary tool on user identification is already well known. It ... more The use of soft biometrics as an auxiliary tool on user identification is already well known. It is not, however, the only use possible for biometric data, as such data can be adequate to get low level information from the user that are not only related to his identity. Gender, hand-orientation and emotional state are some examples, which it can be called soft-biometrics. It is very common to find work using physiologic modalities for softbiometric prediction, but the behavioural data is often neglected. Two possible behavioural modalities that are not often found in the literature are keystroke dynamics and handwriting signature, which can be seen used alone to predict the users gender, but not in any kind of combination scenario. In order to fill this space, this study aims to investigate whether the combination of those two different biometric modalities can impact the gender prediction accuracy, and how this combination should be done.
Brazilian Journal of Development, 2019
New Generation Computing
This Special Issue presents papers selected from the 5th Brazilian Conference on Intelligent Syst... more This Special Issue presents papers selected from the 5th Brazilian Conference on Intelligent Systems (BRACIS), which was held in Recife (Pernambuco), Brazil, from 09 to 12 October, 2016. BRACIS is sponsored by the Brazilian Computer Society (SBC) and it covers topics related to Artificial Neural Networks, Evolutionary Computation, Fuzzy Systems and other models of computational intelligence. The emphasis of BRACIS is on original theories and novel applications of these models, and the proceedings are traditionally published by the IEEE Computer Society Press. BRACIS has an international Program Committee, which includes well-established researchers from Brazil and abroad. The papers submitted to BRACIS 2016 represented a broad range of research developed in Brazil and other countries. In 2016, a total of 176 submissions were received. After a rigorous review process, 76 papers have been accepted for publication in the IEEE proceedings. The papers with best reviews were then invited to submit an extended version for this Special Issue. The contents of each invited paper had to be substantially expanded, and the main focus of the special issue reviewers was in the originality, significance, and technical contribution of the extended papers. At the end of a rigorous reviewing process, four papers have been selected to be published in this Special Issue. The scope of the four selected papers can be considered as interdisciplinary, ranging from Statistics and Computing to Engineering. In the first paper, we turn our
A 3D serious game for medical students training in clinical cases
2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH), 2016
Analyzing the Performance of an Agent-based Neural System for Classification Tasks Using Data Distribution among the Agents
The 2006 Ieee International Joint Conference on Neural Network Proceedings, 2006
Abstract The use of intelligent agents in the structure of multi-classifier systems has been inve... more Abstract The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system has presented good results in some conventional (centralized) classification tasks. Nevertheless, in some classification tasks, relevant features can be distributed over a set of agent. These applications can be classified as distributed ...
This paper presents the general aspects that motivated the construction of the MASim methodology,... more This paper presents the general aspects that motivated the construction of the MASim methodology, aimed for development of agent-based simulations. MASim employs features common to the development of agent-based software as well as to the development of simulation models. MASim is described in terms of agent-based concepts. It also borrows concepts used in mainstream software engineering process frameworks, defining workflows where users, simulation modelers, software developers, testers and experts of the simulation domain collaborate with the purpose of streamlining the development and reuse of simulations and agent components.
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Papers by Anne Magaly de Paula Canuto