Ricardo José Gabrielli Barreto Campello
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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Ricardo J. G. B. Campello received the BSc degree in Electronics Engineering from the State University of São Paulo (Unesp), Ilha Solteira/SP - Brazil, in 1994, and the MSc and Ph.D. degrees in Electrical Engineering from the School of Electrical and Computer Engineering of the State University of Campinas (Unicamp), Campinas/SP - Brazil, in 1997 and 2002, respectively. In 2002 he was a visiting scholar at the Laboratoire D?Informatique, Signaux et Systèmes de Sophia Antipolis, Université de Nice - Sophia Antipolis (UNSA), France. From 2003 through 2006 he worked as an assistant professor in the Graduate Program in Informatics of the Catholic University of Santos, Santos/SP - Brazil, and as an invited researcher of the School of Electrical and Computer Engineering of the State University of Campinas as well. Since 2007 he is with the Department of Computer Sciences of the University of São Paulo (USP) at São Carlos/SP - Brazil, currently as an Associate Professor (from 2011 on). He has also been a merit scholar of the Brazilian National Research Council since 2005. His current research interests fall primarily into the areas of Soft Computing (especially Fuzzy Systems and Evolutionary Computation), Data Mining, Machine Learning, and Modeling of Dynamic Systems. (Source: Lattes Curriculum)
- Methods and algorithms in unsupervised and semi-supervised machine learning, AP.R
Abstract
This document formalizes a research proposal for funding from the Research Foundation of the State of São Paulo (FAPESP). The main objective is to support the research group of the applicant for a period of two years. The group is mainly constituted by the researcher and his students at ICMC / USP, but the project will also involve international collaborations with the University of Alb...
- Control of a bioprocess using orthonormal basis function fuzzy models, AR.EXT
- Evaluation, model selection and unsupervised outlier detection in data spaces and subspaces, BP.DR
Abstract
Outlier detection plays an important role in the pattern discovery from data that can be considered exceptional in some sense. Detecting such patterns is relevant in general because in many data mining applications, such patterns represent extraordinary behaviors that is worth further analysis. An important distinction is that between the supervised and unsupervised techniques. In this ...
- Systematic evaluation of bi-clustering techniques, BP.MS
Abstract
Cluster analysis is a fundamental problem of unsupervised machine learning where the objective is to determine categories that describe a set of objects according to their similarities and inter-relationships. In the traditional formulation of the problem one seeks partitions or hierarchies of partitions containing groups whose objects are in some way similar among themselves and dissim...
- Active, semi-supervised learning for classification and clustering, BP.MS
Abstract
There are many situations in machine learning in which it is easy to obtain vast amounts of unlabeled data, but it is costly to obtain labels for these data. This leads to typical situations in which only a small subset of labeled data is available, which is not representative enough for learning a model in a supervised way. In such situations, techniques that are able to learn from bot...
(Only some records are available in English at this moment)
- Study and development of validation methods for density- and graph-based clustering techniques, BE.PQ
Abstract
This document describes a research proposal for a 1 year sabbatical period under the supervision of Prof. Joerg Sander from the Databases (DB) Group of the Department of Computing Science of the University of Alberta (U of A), in Edmonton, Canada. The preliminary conception of this proposal was envisaged during a 1 week visit of Ricardo J. G. B. Campello (candidate) to the DB group of t...
| 4 / 2 | Completed research grants |
| 10 / 6 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
| 15 / 9 | All research grants and scholarships |
| Associated processes | |
(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)
| Publications | 12 |
| Citations | 354 |
| Cit./Article | 29.5 |
| Data from Web of Science | |
DA ROSA, ALEX; CAMPELLO, RICARDO J. G. B.; AMARAL, WAGNER C.. Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions. IEEE Transactions on Automatic Control, v. 54, n. 12, p. 2757-2772, DEC 2009. Web of Science Citations: 12.
CAMPELLO‚ R.J.G.B.. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. PATTERN RECOGNITION LETTERS, v. 28, n. 7, p. 833-841, 2007.
CAMPOS, GUILHERME O.; ZIMEK, ARTHUR; SANDER, JORG; CAMPELLO, RICARDO J. G. B.; MICENKOVA, BARBORA; SCHUBERT, ERICH; ASSENT, IRA; HOULE, MICHAEL E.. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. DATA MINING AND KNOWLEDGE DISCOVERY, v. 30, n. 4, p. 891-927, JUL 2016. Web of Science Citations: 87. (13/18698-4)
JASKOWIAK, PABLO A.; CAMPELLO, RICARDO J. G. B.; COSTA, IVAN G.. On the selection of appropriate distances for gene expression data clustering. BMC Bioinformatics, v. 15, n. 2, JAN 24 2014. Web of Science Citations: 40. (11/04247-5, 12/15751-9)
JASKOWIAK, PABLO A.; CAMPELLO, RICARDO J. G. B.; COSTA, IVAN G.. Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v. 10, n. 4, p. 845-857, JUL-AUG 2013. Web of Science Citations: 19. (11/04247-5)
DE SOUTO, MARCILIO C. P.; JASKOWIAK, PABLO A.; COSTA, IVAN G.. Impact of missing data imputation methods on gene expression clustering and classification. BMC Bioinformatics, v. 16, FEB 26 2015. Web of Science Citations: 21. (11/04247-5)
NALDI, M. C.; CAMPELLO, R. J. G. B.. Comparison of distributed evolutionary k-means clustering algorithms. Neurocomputing, v. 163, n. SI, p. 78-93, SEP 2 2015. Web of Science Citations: 18. (13/18698-4)
JASKOWIAK, PABLO A.; MOULAVI, DAVOUD; FURTADO, ANTONIO C. S.; CAMPELLO, RICARDO J. G. B.; ZIMEK, ARTHUR; SANDER, JOERG. On strategies for building effective ensembles of relative clustering validity criteria. KNOWLEDGE AND INFORMATION SYSTEMS, v. 47, n. 2, p. 329-354, MAY 2016. Web of Science Citations: 10. (10/20032-6, 11/04247-5, 12/15751-9)
HORTA, DANILO; CAMPELLO, RICARDO J. G. B.. Comparing Hard and Overlapping Clusterings. JOURNAL OF MACHINE LEARNING RESEARCH, v. 16, p. 2949-2997, DEC 2015. Web of Science Citations: 3. (13/18698-4, 09/17469-6)
JASKOWIAK, PABLO ANDRETTA; COSTA, IVAN G.; CAMPELLO, RICARDO J. G. B.. Clustering of RNA-Seq samples: Comparison study on cancer data. METHODS, v. 132, p. 42-49, JAN 1 2018. Web of Science Citations: 3. (11/04247-5)
CAMPELLO, RICARDO J. G. B.; MOULAVI, DAVOUD; ZIMEK, ARTHUR; SANDER, JOERG. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, v. 10, n. 1, JUL 2015. Web of Science Citations: 96. (13/18698-4, 10/20032-6)
CAMPELLO, R. J. G. B.. Generalized external indexes for comparing data partitions with overlapping categories. PATTERN RECOGNITION LETTERS, v. 31, n. 9, p. 966-975, JUL 1 2010. Web of Science Citations: 22.
PADILHA, VICTOR A.; CAMPELLO, RICARDO J. G. B.. A systematic comparative evaluation of biclustering techniques. BMC Bioinformatics, v. 18, JAN 23 2017. Web of Science Citations: 23. (13/18698-4, 14/08840-0)
(References retrieved automatically from State of São Paulo Research Institutions)
JASKOWIAK, Pablo Andretta. Sobre a avaliação de resultados de agrupamento: medidas, comitês e análise de dados de expressão gênica. 2015. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
HORTA, Danilo. Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BATISTA, Antônio José de Lima. Classificação semi-supervisionada ativa baseada em múltiplas hierarquias de agrupamento. 2016. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
HORTA, Danilo. Abordagens evolutivas para agrupamento relacional de dados. 2010. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
NALDI, Murilo Coelho. Técnicas de combinação para agrupamento centralizado e distribuído de dados. 2011. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
PADILHA, Victor Alexandre. Avaliação sistemática de técnicas de bi-agrupamento de dados. 2016. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CAMPELLO, Ricardo José Gabrielli Barreto. Arquiteturas e metodologias para modelagem e controle de sistemas complexos utilizando ferramentas classicas e modernas. 2002. Tese (Doutorado) – Faculdade de Engenharia Eletrica e de Computação. Universidade Estadual de Campinas.