Aurora Pozo

Universidade Federal do Paraná

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Sílvia Regina Vergílio

Universidade Federal do Paraná

Roberto Santana

University of the Basque Country

Olacir R. Castro

Universidade Federal do Paraná

Thelma Elita Colanzi

Universidade Estadual de Maringá

Wesley K. G. Assunção

North Carolina State University

Richard A. Gonçalves

Institut de Pharmacologie Moléculaire et Cellulaire

Elias P. Duarte

Universidade Federal do Paraná

André B. de Carvalho

Universidade Federal de Sergipe

Gian Fritsche

Universidade Federal do Paraná

Cristina Y. Morimoto

Universidade Federal do Paraná

Cooperative Institutions

Universidade Federal do Paraná

Universidade Tecnológica Federal do Paraná

Universidade Federal do Pará

Universidade de São Paulo

Centre National de la Recherche Scientifique

Universidade Estadual do Centro-Oeste

Laboratoire d'Informatique de Paris-Nord

Brazilian Society of Computational and Applied Mathematics

University of the Basque Country

Universidade Estadual de Maringá

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Using Correlation to Improve Boosting Technique: An Application for Time Series Forecasting

Proceedings - International Conference on Tools with Artificial Intelligence, TAI (2006)

Luzia De Souza Aurora Pozo Anselmo Frizera

Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming and Boosting technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a Boosting technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.

Boosting

Gradient boosting

10.1109/ictai.2006.118

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Citations (3)

The Analysis of a Cooperative Hyper-Heuristic on a Constrained Real-world Many-objective Continuous Problem

2022 IEEE Congress on Evolutionary Computation (CEC) (2020)

Gian Fritsche Aurora Pozo

Despite the significant number of strategies proposed for many-objective optimization, most studies lack the evaluation of their algorithms in real-world applications. In this research, we present the analysis of the application of a recently proposed hyper-heuristic for many-objective optimization to the wind turbine design problem. This application is a representative problem for renewable energy, with 32 variables, 22 constraints, and five objectives. In this study, we solved this problem using the cooperative hyper-heuristic (HH-CO), which uses eight MOEAs on its pool, and switches among them during the search. These MOEAs are representative strategies to deal with multi and many-objective optimization; within HH-CO, every MOEA has its internal population. This behavior is particularly suitable for many-objective optimization, where each MOEA has its criteria to select the solutions that will be preserved for the next generation. Besides, the MOEAs exchange information after being applied, with this migration step, all MOEAs update their private information even if they were not employed. We also evaluate the set of eight state-of-the-art multi-objective evolutionary algorithms (MOEAs), adapted to handle constraints. The empirical analysis uses the hypervolume (HV) quality indicator to evaluate the results and, the HH-CO achieved results competitive to the best MOEAs.

10.1109/cec48606.2020.9185904

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Citations (7)

Iterated multi-swarm

André Britto Sanaz Mostaghim Aurora Pozo

Usually, Multi-Objective Evolutionary Algorithms face serious challengers in handling many objectives problems. This work presents a new Particle Swarm Optimization algorithm, called Iterated Multi-Swarm (I-Multi Swarm), which explores specific characteristics of PSO to face Many-Objective Problems. The algorithm takes advantage of a Multi-Swarm approach to combine different archiving methods aiming to improve convergence to the Pareto-optimal front and diversity of the non-dominated solutions. I-Multi Swarm is evaluated through an empirical analysis that uses a set of many-Objective problems, quality indicators and statistical tests.

10.1145/2463372.2463447

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Citations (9)

Towards a Better Understanding of Heuristic Approaches Applied to the Biological Motif Discovery

Lecture notes in computer science (2022)

Jader M. Caldonazzo Garbelini Danilo Sipoli Sanches Aurora Pozo

Heuristics

Constructive

Maximization

10.1007/978-3-031-21686-2_13

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Citations (2)

Preface

Natural Computing (2018)

Aurora Pozo Heloisa A. Camargo

10.1007/s11047-018-9680-3

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Citations (0)

Uma proposta para Federated Learning em Cibersegurança

Bruno Meyer Aurora Pozo Michele Nogueira Wagner M. Nunan Zola

Neste artigo, propõem-se dois métodos de votação para Ensemble Learning em Federated Learning e Cibersegurança. As propostas consideram características em dados de cibersegurança e aumentam a eficiência de sistemas que classificam tráfego de rede.

Ensemble Learning

10.5753/eradrs.2023.229700

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Técnicas de aprendizado de máquinas aplicadas à classificação de decisões judiciais

Revista de Estudos Empíricos em Direito (2023)

Dimmy Magalhães Aurora Pozo Sidnei Machado

A análise de processos judiciais é uma tarefa cara, que requer muito tempo de juizes e assessores, seja para tomar decisões, seja para classificar de acordo com a jurisprudência vigente. Porém, esse processo é repetitivo e extrair a semântica desse corpus pode ser uma etapa de apoio a esse processo. O objetivo desta pesquisa é desenvolver uma metodologia capaz de gerar automaticamente classificações de documentos jurídicos, utilizando técnicas de processamento de linguagem natural. Primeiramente, coletamos 430.000 sentenças de tribunais trabalhistas brasileiros de 2006 a 2018. Então propomos o uso de técnicas de geração de representação de palavras para representação de dados. Em seguida, usamos técnicas de agrupamento para agrupar semanticamente as decisões judiciais semelhantes. Finalmente, os grupos são usados para criar rótulos artificiais para cada documento. Por fim, utilizamos técnicas de classificação para produzir modelos capazes de captar a semântica do texto judicial. Os resultados são promissores na captura do contexto semântico dos textos jurídicos e, portanto, essa metodologia pode ser utilizada como suporte para o processo decisório brasileiro.

10.19092/reed.v9.573

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Citations (2)

Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach

Studies in computational intelligence (2009)

André B. de Carvalho Aurora Pozo

10.1007/978-3-642-03625-5_8

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Citations (4)

Genetic algorithm restricted by tabu lists in data mining

Fabrício Martins Lopes Aurora Pozo

The present work shows an implementation of a genetic algorithm (GA) integrated with tabu lists to generate a classifier tool for a data mining task. The choice of the GA paradigm is partially justified by its great capacity to deal with noise, invalid or inexact data, and its easy adaptation to different data domains. The GA algorithm uses a tabu list to restrict the selection process. This restriction allows the creation of set of potential rules for the classifier tool. This strategy was proposed by Kurahashi et al. (2000), for multimodal and multiobjective function optimization and represents an alternative to sharing methods. In this work, the behavior of this approach in data mining task was analyzed. Experiments were performed on five databases and results were compared with 34 other classifying algorithms. After that, noise was added to the databases and a new set of experiments was performed. The results show that the algorithm proposed is efficient and robust. And the strategy used to maintain the diversity was considered valid, since the algorithm was able to keep its accuracy in categorization even for smaller populations.

Noisy data

10.1109/sccc.2001.972646

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Citations (1)

Search Based Software Engineering: Review and analysis of the field in Brazil

Journal of Systems and Software (2012)

Thelma Elita Colanzi Sílvia Regina Vergílio Wesley K. G. Assunção Aurora Pozo

Search-based software engineering

Consolidation

10.1016/j.jss.2012.07.041

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Citations (34)