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On the parameter optimization in machine learning techniques: advances and paradigms


Machine learning techniques have been actively employed in the last years in several research areas. Although such techniques are known for their good generalization skills, some of them are parameter-dependent, which may result in a high computational burden for learning such set of parameters, making them inviable for large-scale problems, as well as for real-time and prompt responses. This proposal aims at studying meta-heuristic optimization techniques, as well as their applications for machine learning optimization techniques, such as model and kernel selection, feature selection and information fusion, among others. This project also aims at investigating the suitability of optimizing parameters in the context of deep learning architectures, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Deep Belief Networks and Auto-encoders. The idea is to focus on multi-objective optimization techniques, since just a few number of works have addressed such context. The proposal has the collaboration of Prof. Xin-She Yang from Middlesex University, England, which is a very active researcher on meta-heuristic-based optimization. The main idea is to validate his new algorithms in the context os this project. (AU)

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Scientific publications (37)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PEREIRA, DANILLO ROBERTO; PAPA, JOAO PAULO; ROSALIN SARAIVA, GUSTAVO FRANCISCO; SOUZA, GUSTAVO MAIA. Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 145, p. 35-42, . (14/12236-1, 13/07375-0, 16/19403-6, 14/16250-9)
DE ROSA, GUSTAVO H.; PAPA, JOAO P.; YANG, XIN-S. Handling dropout probability estimation in convolution neural networks using meta-heuristics. SOFT COMPUTING, v. 22, n. 18, SI, p. 6147-6156, . (14/12236-1, 14/16250-9, 15/25739-4)
AMORIM, WILLIAN P.; FALCAO, ALEXANDRE X.; PAPA, JOAO P.. Multi-label semi-supervised classification through optimum-path forest. INFORMATION SCIENCES, v. 465, p. 86-104, . (14/16250-9, 14/12236-1, 13/20387-7)
IWASHITA, ADRIANA SAYURI; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO PAULO. Learning concept drift with ensembles of optimum-path forest-based classifiers. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, v. 95, p. 198-211, . (13/07375-0, 14/16250-9, 14/12236-1)
NACHIF FERNANDES, SILAS EVANDRO; PAPA, JOAO PAULO. Improving optimum-path forest learning using bag-of-classifiers and confidence measures. PATTERN ANALYSIS AND APPLICATIONS, v. 22, n. 2, p. 703-716, . (13/07375-0, 14/16250-9, 14/12236-1, 16/19403-6)
NACHIF FERNANDES, SILAS EVANDRO; DE SOUZA, ANDRE NUNES; GASTALDELLO, DANILO SINKITI; PEREIRA, DANILLO ROBERTO; PAPA, JOAO PAULO. Pruning optimum-path forest ensembles using metaheuristic optimization for land-cover classification. International Journal of Remote Sensing, v. 38, n. 20, p. 5736-5762, . (14/16250-9, 14/12236-1)
CULQUICONDOR, ALDO; BALDASSIN, ALEXANDRO; CASTELO-FERNANDEZ, CESAR; DE CARVALHO, JOAO P. L.; PAPA, JOAO PAULO. An efficient parallel implementation for training supervised optimum-path forest classifiers. Neurocomputing, v. 393, p. 259-268, . (14/16250-9, 14/12236-1, 13/07375-0, 16/19403-6, 17/03940-5)
PEREIRA, CLAYTON R.; PEREIRA, DANILO R.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO P.. A survey on computer-assisted Parkinson's Disease diagnosis. ARTIFICIAL INTELLIGENCE IN MEDICINE, v. 95, p. 48-63, . (13/07375-0, 14/16250-9, 14/12236-1, 16/19403-6)
REBOUCAS FILHO, PEDRO P.; DA SILVA BARROS, ANTONIO C.; RAMALHO, GERALDO L. B.; PEREIRA, CLAYTON R.; PAPA, JOAO PAULO; DE ALBUQUERQUE, VICTOR HUGO C.; TAVARES, JOAO MANUEL R. S.. Automated recognition of lung diseases in CT images based on the optimum-path forest classifier. NEURAL COMPUTING & APPLICATIONS, v. 31, n. 2, p. 901-914, . (14/16250-9, 14/12236-1)
PEREIRA, LUIS A. M.; PAPA, JOAO P.; COELHO, ANDRE L. V.; LIMA, CLODOALDO A. M.; PEREIRA, DANILLO R.; DE ALBUQUERQUE, VICTOR HUGO C.. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. NEURAL COMPUTING & APPLICATIONS, v. 31, n. 2, p. 1317-1329, . (14/16250-9, 09/16206-1, 11/14094-1)
PEREIRA, CLAYTON R.; PEREIRA, DANILO R.; ROSA, GUSTAVO H.; ALBUQUERQUE, VICTOR H. C.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO P.. Handwritten dynamics dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. ARTIFICIAL INTELLIGENCE IN MEDICINE, v. 87, p. 67-77, . (13/07375-0, 14/16250-9, 16/19403-6, 10/15566-1, 14/12236-1, 15/25739-4)
IWASHITA, ADRIANA SAYURI; PAPA, JOAO PAULO. An Overview on Concepts Drift Learning. IEEE ACCESS, v. 7, p. 1532-1547, . (14/16250-9, 14/12236-1, 13/07375-0, 16/19403-6)
SAITO, PRISCILA T. M.; NAKAMURA, RODRIGO Y. M.; AMORIM, WILLIAN P.; PAPA, JOAO P.; DE REZENDE, PEDRO J.; FALCAO, ALEXANDRE X.. Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint. PLoS One, v. 10, n. 6, . (14/16250-9, 11/14058-5, 13/20387-7, 07/52015-0, 12/18768-0)
PAPA, JOAO PAULO; ROSA, GUSTAVO H.; PEREIRA, DANILLO R.; YANG, XIN-SHE. Quaternion-based Deep Belief Networks fine-tuning. APPLIED SOFT COMPUTING, v. 60, p. 328-335, . (14/16250-9, 14/12236-1, 15/25739-4)
AMORIM, WILLIAN P.; FALCAO, ALEXANDRE X.; PAPA, JOAO P.; CARVALHO, MARCELO H.. Improving semi-supervised learning through optimum connectivity. PATTERN RECOGNITION, v. 60, p. 72-85, . (14/16250-9, 13/20387-7)
PEREIRA, DANILLO R.; PAZOTI, MARIO A.; PEREIRA, LUIS A. M.; RODRIGUES, DOUGLAS; RAMOS, CAIO O.; SOUZA, ANDRE N.; PAPA, JOAO P.. Social-Spider Optimization-based Support Vector Machines applied for energy theft detection. COMPUTERS & ELECTRICAL ENGINEERING, v. 49, p. 25-38, . (14/16250-9, 12/06472-9, 13/20387-7)
PAPA, JOAO PAULO; NACHIF FERNANDES, SILAS EVANDRO; FALCAO, ALEXANDRE XAVIER. Optimum-Path Forest based on k-connectivity: Theory and applications. PATTERN RECOGNITION LETTERS, v. 87, n. SI, p. 117-126, . (14/16250-9, 09/16206-1, 13/20387-7)
PIRES, RAFAEL G.; PEREIRA, DANILLO R.; PEREIRA, LUIS A. M.; MANSANO, ALEX F.; PAPA, JOO P.. Projections onto convex sets parameter estimation through harmony search and its application for image restoration. NATURAL COMPUTING, v. 15, n. 3, SI, p. 493-502, . (14/16250-9, 09/16206-1, 11/14094-1, 13/20387-7)
PEREIRA, DANILLO R.; PISANI, RODRIGO J.; DE SOUZA, ANDRE N.; PAPA, JOAO P.. An Ensemble-Based Stacked Sequential Learning Algorithm for Remote Sensing Imagery Classification. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v. 10, n. 4, p. 1525-1541, . (14/16250-9, 13/20387-7, 14/12236-1)
RODRIGUES, DOUGLAS; SILVA, GABRIEL F. A.; PAPA, JOAO P.; MARANA, APARECIDO N.; YANG, XIN-SHE. EEG-based person identification through Binary Flower Pollination Algorithm. EXPERT SYSTEMS WITH APPLICATIONS, v. 62, p. 81-90, . (14/16250-9)
TURESSON, HJALMAR K.; RIBEIRO, SIDARTA; PEREIRA, DANILLO R.; PAPA, JOAO P.; DE ALBUQUERQUE, VICTOR HUGO C.. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations. PLoS One, v. 11, n. 9, . (14/16250-9, 13/07699-0, 15/50319-9)
PAPA, JOAO PAULO; ROSA, GUSTAVO HENRIQUE; PAPA, LUCIENE PATRICI. A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes. PATTERN RECOGNITION LETTERS, v. 100, p. 59-66, . (13/07375-0, 14/16250-9, 16/19403-6, 10/15566-1, 14/12236-1, 15/25739-4)
OSAKU, DANIEL; PEREIRA, DANILLO R.; LEVADA, ALEXANDRE L. M.; PAPA, JOAO P.. Fine-Tuning Contextual-Based Optimum-Path Forest for Land-Cover Classification. IEEE Geoscience and Remote Sensing Letters, v. 13, n. 5, p. 735-739, . (14/16250-9, 12/06472-9, 13/20387-7)
PEREIRA, DANILLO ROBERTO; PAPA, JOAO PAULO. A new approach to contextual learning using interval arithmetic and its applications for land-use classification. PATTERN RECOGNITION LETTERS, v. 83, n. 2, p. 188-194, . (15/50319-9, 14/16250-9)
PEREIRA, CLAYTON R.; PEREIRA, DANILO R.; SILVA, FRANCISCO A.; MASIEIRO, JOAO P.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO P.. A new computer vision-based approach to aid the diagnosis of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 136, p. 79+, . (13/20387-7, 14/16250-9, 09/16206-1)
PASSOS JUNIOR, LEANDRO APARECIDO; OBA RAMOS, CAIO CESAR; RODRIGUES, DOUGLAS; PEREIRA, DANILLO ROBERTO; DE SOUZA, ANDRE NUNES; PONTARA DA COSTA, KELTON AUGUSTO; PAPA, JOAO PAULO. Unsupervised non-technical losses identification through optimum-path forest. Electric Power Systems Research, v. 140, p. 413-423, . (12/14158-2, 09/16206-1, 13/20387-7, 15/00801-9, 14/16250-9)
PAPA, JOAO P.; ROSA, GUSTAVO H.; MARANA, APARECIDO N.; SCHEIRER, WALTER; COX, DAVID D.. Model selection for Discriminative Restricted Boltzmann Machines through meta-heuristic techniques. JOURNAL OF COMPUTATIONAL SCIENCE, v. 9, n. SI, p. 14-18, . (14/16250-9, 13/20387-7)
PEREIRA, DANILLO R.; DELPIANO, JOSE; PAPA, JOAO P.. On the optical flow model selection through metaheuristics. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, . (14/16250-9, 13/20387-7)
OSAKU, D.; NAKAMURA, R. Y. M.; PEREIRA, L. A. M.; PISANI, R. J.; LEVADA, A. L. M.; CAPPABIANCO, F. A. M.; FALCO, A. X.; PAPA, JOAO P.. Improving land cover classification through contextual-based optimum-path forest. INFORMATION SCIENCES, v. 324, p. 60-87, . (14/16250-9, 09/16206-1, 12/06472-9, 13/20387-7)
PAPA, JOAO PAULO; SCHEIRER, WALTER; COX, DAVID DANIEL. Fine-tuning Deep Belief Networks using Harmony Search. APPLIED SOFT COMPUTING, v. 46, p. 875-885, . (13/20387-7, 14/16250-9)
FERNANDES, SILAS E. N.; PEREIRA, DANILLO R.; RAMOS, CAIO C. O.; SOUZA, ANDRE N.; GASTALDELLO, DANILO S.; PAPA, JOAO P.. A Probabilistic Optimum-Path Forest Classifier for Non-Technical Losses Detection. IEEE TRANSACTIONS ON SMART GRID, v. 10, n. 3, p. 3226-3235, . (13/07375-0, 14/16250-9, 17/02286-0, 16/19403-6, 14/12236-1)
PASSOS, JR., LEANDRO APARECIDO; PAPA, JOAO PAULO. Temperature-Based Deep Boltzmann Machines. NEURAL PROCESSING LETTERS, v. 48, n. 1, p. 95-107, . (14/16250-9, 14/12236-1, 16/19403-6)
GUIMARAES, RANIERE ROCHA; PASSOS JR, LEANDRO A.; HOLANDA FILHO, RAIMIR; DE ALBUQUERQUE, VICTOR HUGO C.; RODRIGUES, JOEL J. P. C.; KOMAROV, MIKHAIL M.; PAPA, JOAO PAULO. Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering. IEEE NETWORK, v. 33, n. 2, p. 126-131, . (13/07375-0, 14/16250-9, 14/12236-1, 16/19403-6)
PASSOS, LEANDRO A.; DE SOUZA, JR., LUIS A.; MENDEL, ROBERT; EBIGBO, ALANNA; PROBST, ANDREAS; MESSMANN, HELMUT; PALM, CHRISTOPH; PAPA, JOAO PAULO. Barrett's esophagus analysis using infinity Restricted Boltzmann Machines. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 59, p. 475-485, . (14/16250-9, 14/12236-1, 13/07375-0, 15/25739-4, 16/21243-7)
DE ALBUQUERQUE, VICTOR HUGO C.; NUNES, THIAGO M.; PEREIRA, DANILLO R.; LUZ, EDUARDO JOSE DA S.; MENOTTI, DAVID; PAPA, JOAO P.; TAVARES, JOAO MANUEL R. S.. Robust automated cardiac arrhythmia detection in ECG beat signals. NEURAL COMPUTING & APPLICATIONS, v. 29, n. 3, p. 679-693, . (14/16250-9)
RODRIGUES, DOUGLAS; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO PAULO. A multi-objective artificial butterfly optimization approach for feature selection. APPLIED SOFT COMPUTING, v. 94, . (14/12236-1, 14/16250-9, 17/02286-0, 16/19403-6)

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