Márcio Porto Basgalupp
CV Lattes
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Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. Instituto de Ciência e Tecnologia (ICT) (Institutional affiliation for the last research proposal) Birthplace: Brazil
has graduation at Bacharelado at Ciência da Computação by Universidade Federal de Pelotas (2007), master's at Ciência da Computação by Pontifícia Universidade Católica do Rio Grande do Sul (2009), and Ph.D. at Ciências da Computação e Matemática Computacional by Universidade de São Paulo (2013). Currently is Associate Professor at Institute of Science and Techonology at Federal University of Sao Paulo (UNIFESP). Has coordinated national and international research projects funded by CNPq and FAPESP. Has experience in the area of Computer Science, with emphasis on Machine Learning, Data Mining and Bio-inspired Computation. Since 2014, is granted by CNPq with a Research Productivity Award. (Source: Lattes Curriculum)
- Hyper-heuristics and decision trees for hierarchical multi-label classification problems, AV.EXT
Abstract
Hierarchical Multi-label Classification (HMC) is a complex problem, in which classes involved are structured in a hierarchy with hundreds or even thousands of classes. Additionally, instances can be simultaneously classified into more than one path in this hierarchy. These problems are very common, for example, in protein function prediction and annotation of medical images. Among the d...
- Multi-objective hyper-heuristics for automatic design of multi-test decision tree induction algorithms, AP.R
Abstract
Decision-tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. In previous work, we proposed t...
- Evolving decision-tree induction algorithms with a multi-objective hyper-heuristic using the Pareto dominance approach, AV.EXT
Abstract
The goal of the proposed research collaboration is to design, develop and evaluate an improved decision-tree induction algorithm with a multi-objective hyper-heuristic using the Pareto dominance approach. The previous results have shown the advantages of evolving a decision-tree induction algorithm with a hyper-heuristic regarding the classification performance. However, the classificat...
(Only some records are available in English at this moment)
- Development of the probabilistic linear genetic programming technique and application on Kaizen programming for supervised machine learning, BP.DD
Abstract
Linear Genetic Programming is a type of algorithm able to evolve code in a programming language. LGP has been applied in a wide range of problems, as machine learning, navigation, and routing. Its stochastic search process has no knowledge to generate good solutions nor is able of avoiding poor ones, which decreases its efficiency. In this project, two topics will be explored in order t...
- Grammatical evolution for automatic construction of similarity functions in the context of semi-supervisioned learning, BP.MS
Abstract
In the context of machine learning, representing a dataset by graphs have been studied in the literature, especially in the semi supervised learning area. The principal feature of the techniques based on graphs (networks) is in the way data is represented in which network vertices represent the data and the edges represent the distances/similarities (relations) between the examples. Amo...
- Identification of different alternatives for building blocks of genetic programming algorithm for evolving decision tree induction algorithms, BP.IC
Abstract
The identification of macro-parameters (used as building-blocks) is one of the most important tasks for automatically evolving decision-tree induction algorithms. These building-blocks will be used by the GP for automatically evolving new decision-tree induction algorithms. In this context, this work proposes to investigate other building-blocks as well as some variations of the ones pr...
- Development of a linear genetic programming algorithm using an estimation of distribution algorithm applied to supervised machine learning, BE.EP.DD
Abstract
Linear Genetic Programming (LGP) is a kind of algorithm capable of evolving computer code which has been successfully applied in various problems, such as Machine Learning (ML), navigation, and routing. As in other Evolutionary Algorithms (EAs), its stochastic search process neither has the knowledge to produce good quality solutions nor is able to avoid poor quality solutions, which re...
- Incorporating multi-test decision trees into the HEAD-DT algorithm, BE.PQ
Abstract
Decision-tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. In previous work, we proposed t...
| 5 / 5 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 4 / 2 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 12 / 10 | 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 | 3 |
| Citations | 21 |
| Cit./Article | 7.0 |
| Data from Web of Science | |
DAL PICCOL SOTTO, LEO FRANCOSO; DE MELO, VINICIUS VELOSO; BASGALUPP, MARCIO PORTO. -LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem. KNOWLEDGE AND INFORMATION SYSTEMS, v. 52, n. 2, p. 445-465, AUG 2017. Web of Science Citations: 2. (13/20606-0, 16/07095-5)
BARROS, RODRIGO C.; BASGALUPP, MARCIO P.; DE CARVALHO, ANDRE C. P. L. F.. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification. Genetic Programming and Evolvable Machines, v. 16, n. 3, p. 241-281, SEP 2015. Web of Science Citations: 1. (09/14325-3)
BARROS, RODRIGO C.; BASGALUPP, MARCIO P.; FREITAS, ALEX A.; DE CARVALHO, ANDRE C. P. L. F.. Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, v. 18, n. 6, p. 873-892, DEC 2014. Web of Science Citations: 18. (09/14325-3)
(References retrieved automatically from State of São Paulo Research Institutions)
BASGALUPP, Márcio Porto. LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão. 2010. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.